Risk-aware policy evaluation using agent-based simulationBruce Edmonds
A talk about how modelling of complex issues of policy relevance. It covers some of the tensions and difficulties, as well as some of the unrealistic expectations of this kind of modelling. Rather it is suggested these kinds of model should be used as a kind of risk-analysis. Two examples of this are given.
Talk given in Reykjavik at University of Iceland, 30th Nov 2016.
Towards Institutional System Farming
A talk at the Lorentz Workshop on "Emerging Institutions: Design or Evolution?" September 2016, Leiden, NL (https://www.lorentzcenter.nl/lc/web/2016/836/info.php3?wsid=836&venue=Oort)
Mixing fat data, simulation and policy - what could possibly go wrong?Bruce Edmonds
A talk given at the CECAN workshop on "What Good Data could do for Evaluation" at the Alan Turing Institute, 25th Feb. 2019.
Abstract:
In complex situations (which includes most where humans are involved) it is infeasible to predict the impact of any particular policy (or even what is probable). Randomised Control Trials do not tell one: what kinds of situation a policy might work in, what are enablers and inhibitors of the effectiveness of a policy. Here I suggest that using 'fat' data and simulation might allow a possibilistic analysis of policy impact - namely an exploration of what could go surprisingly wrong (or indeed right). Whilst this does not allow the optimisation of policy, it does inform the effective monitoring of policy, and basic contingency planning. However, this requires a different approach to policy - from planning and optimisation to an adaptive approach, with richer continual monitoring and a readiness to tune or adapt policy as data comes in. Examples of this are given concerning domestic water consumption (in the main talk), and in supplementary slides: voter turnout and fishing.
Possibilistic prediction and risk analyses
A talk given at the EA annual Conference, Bonn, May 2015
Abstract:
It is in the nature of complex systems that predictions that give a probability are not possible.
Indeed I argue that giving "the most likely" or "rough" prediction is more harmful than useful.
Rather an approach which maps out some of the possible outcomes is outlined.
Agent-based modelling is ideal for producing these - including, crucially, possibilities that could not have been conceived just by thinking about it (due to the fact that events can combine in ways that are more complex than the human brain can cope with directly).
A characterisation of the real future possibilities and their nature allows some positive responses to events:
* putting in place 'early warning indicators' for the emergence of identified possibilities
* contingency planning for when they are indicated.
Such an approach would allow policy makers to better 'drive' their decision making, without abnegating responsibility to experts.
Automated decision making using Predictive Applications – Big Data ParisLars Trieloff
Predictive Applications enable automated data-driven decisions using big data, machine learning, artificial intelligence and optimization algorithms. With this, they are able to scale decision making, improve the quality of decisions and circumvent cognitive biases that cloud human decision making.
Automated Decision making with Predictive Applications – Big Data HamburgLars Trieloff
Most businesses are making most decisions the way Lizards do: based on very simple reflex-response patterns and let cognitive biases taint their decision making. Instead of letting gut feel and biases take over, predictive applications make decisions fast, cheap and fact-based.
Risk-aware policy evaluation using agent-based simulationBruce Edmonds
A talk about how modelling of complex issues of policy relevance. It covers some of the tensions and difficulties, as well as some of the unrealistic expectations of this kind of modelling. Rather it is suggested these kinds of model should be used as a kind of risk-analysis. Two examples of this are given.
Talk given in Reykjavik at University of Iceland, 30th Nov 2016.
Towards Institutional System Farming
A talk at the Lorentz Workshop on "Emerging Institutions: Design or Evolution?" September 2016, Leiden, NL (https://www.lorentzcenter.nl/lc/web/2016/836/info.php3?wsid=836&venue=Oort)
Mixing fat data, simulation and policy - what could possibly go wrong?Bruce Edmonds
A talk given at the CECAN workshop on "What Good Data could do for Evaluation" at the Alan Turing Institute, 25th Feb. 2019.
Abstract:
In complex situations (which includes most where humans are involved) it is infeasible to predict the impact of any particular policy (or even what is probable). Randomised Control Trials do not tell one: what kinds of situation a policy might work in, what are enablers and inhibitors of the effectiveness of a policy. Here I suggest that using 'fat' data and simulation might allow a possibilistic analysis of policy impact - namely an exploration of what could go surprisingly wrong (or indeed right). Whilst this does not allow the optimisation of policy, it does inform the effective monitoring of policy, and basic contingency planning. However, this requires a different approach to policy - from planning and optimisation to an adaptive approach, with richer continual monitoring and a readiness to tune or adapt policy as data comes in. Examples of this are given concerning domestic water consumption (in the main talk), and in supplementary slides: voter turnout and fishing.
Possibilistic prediction and risk analyses
A talk given at the EA annual Conference, Bonn, May 2015
Abstract:
It is in the nature of complex systems that predictions that give a probability are not possible.
Indeed I argue that giving "the most likely" or "rough" prediction is more harmful than useful.
Rather an approach which maps out some of the possible outcomes is outlined.
Agent-based modelling is ideal for producing these - including, crucially, possibilities that could not have been conceived just by thinking about it (due to the fact that events can combine in ways that are more complex than the human brain can cope with directly).
A characterisation of the real future possibilities and their nature allows some positive responses to events:
* putting in place 'early warning indicators' for the emergence of identified possibilities
* contingency planning for when they are indicated.
Such an approach would allow policy makers to better 'drive' their decision making, without abnegating responsibility to experts.
Automated decision making using Predictive Applications – Big Data ParisLars Trieloff
Predictive Applications enable automated data-driven decisions using big data, machine learning, artificial intelligence and optimization algorithms. With this, they are able to scale decision making, improve the quality of decisions and circumvent cognitive biases that cloud human decision making.
Automated Decision making with Predictive Applications – Big Data HamburgLars Trieloff
Most businesses are making most decisions the way Lizards do: based on very simple reflex-response patterns and let cognitive biases taint their decision making. Instead of letting gut feel and biases take over, predictive applications make decisions fast, cheap and fact-based.
Mixing ABM and policy...what could possibly go wrong?Bruce Edmonds
Invited talk at 19th International Workshop on Multi-Agent Based Simulation at Stockholm on 14th July 2018.
Mixing ABM and Policy ... what could possibly go wrong?
This talk looks at a number of ways in which using ABM in the context of influencing policy can go wrong: during model construction, with model application and other.
It is related to the book chapter:
Aodha, L. and Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity - a handbook, 2nd edition. Springer, 801-822.
Socio-Ecological Simulation - a risk-assessment approachBruce Edmonds
An invited talk in Tromsoe, 5 June 2018.
Both social and ecological systems are complex, but when they combine (as when human societies farm/hunt) there is a double complexity. This complexity means it is infeasible to predict the outcome of their interaction and unwise to rely on any prediction. An alternative approach is to use complex simulations to try and discover some possible ways that such systems can go wrong. This can reveal risks that other approaches might miss, due to the fact that more of the complexity is included within the model. Once a risk is identified then measures to monitor its emergence can be implemented, allowing the earliest possible warning of this. An example of this approach applied to a fisheries ecosystem is described.
Policy Making using Modelling in a Complex worldBruce Edmonds
A talk given at the CECAN workshop, London July 2016
Abstract:
The consequences of complexity in the real world are discussed together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are fundamentally unpredictable by nature, especially when in the presence of structural change (transitions). This implies consequences for the way we model, but also for the way models are used in the policy process.
I discuss the problems arising from a too narrow focus on quantification in managing complex systems, in particular those of optimisation. I criticise some of the approaches that ignore these difficulties and pretend to approximately forecast using the impact of policy options using over-simple models. However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from "Complexity Science" can help with such management. Managing complex systems requires a good understanding of the dynamics of the system in question - to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent based simulation will be discussed as a tool that is suitable for this task, especially in conjunction with model-informed data visualisation.
Towards Integrating Everything (well at least: ABM, data-mining, qual&quant d...Bruce Edmonds
A talk given at the SKIN3 workshop in Budapest, May 2014 (http://cress.soc.surrey.ac.uk/SKIN/events/third-skin-workshop)
Innovation or other policy-orientated research has tended to take one of two strategies: (a) work with high-level abstractions of macro-level variables or (b) focus on micro-level aspects/areas with simpler mechanisms. Whilst (a) may provide some comfort in the form of forecasts, these are almost useless for policy since they can only be relied upon if nothing much has changed. Although approach (b) may produce some interesting studies which show how complex even small aspects of the involved processes are, with maybe interesting emergent effects, it provides only a small part of the overall picture and little to guide decision making.
Rather, I (with others) suggest a different approach. Instead of aiming to produce some kind of "adequate" theory (usually in the form of a model along with its interpretation), that instead we aim at integrating different kinds of evidence and find the best ways to present these to policy makers in order to help policy-makers 'drive' by providing views of what is happening. Thus (1) utilising the greatest possible range of evidence and (2) providing rich, relevant but synthetic views of this evidence to the policy makers. Any projections should be 'possibilistic' rather than 'probabilistic' - showing the different ways in which social processes might unfold, and help inform the analysis of risks. The talk looks at some of the ways in which this might be done, to integrate micro-level narrative data, time-series data, survey data, network data, big data using a variety of techniques. In this view, models do not disappear, but rather have a different purpose and hence be developed and checked differently.
This shift will involve a change in attitude and approach from both researchers and those in the policy world. Researchers will have to give up the playing for general or abstract theory, satisfying themselves with more gentle and incremental abstraction, whilst also accepting and working with a greater variety of kinds of evidence. They will also have to stop 'conning' the policy world with forecasts, and refuse to provide these as more dangerous than helpful. The policy world will have to stop looking for a magic 'crutch' that will reduce uncertainty (or provide justification for chosen policies) and move towards greater openness with both data and models.
BA and Beyond 19 Sponsor spotlight - Namahn - Beating complexity with complexityBA and Beyond
It’s a complex world full of complex problems- organisational change, the income inequality gap and digital transformation just to name a few.
The conventional way of combatting complexity to solve problems no longer works.
The great minds of Systemic Design have come together to create a unique and innovative toolkit designed to embrace complexity and change the way that we design solutions.
The first of its kind, the toolkit is based on academic research and human-centred design expertise. It is also the first to be endorsed by the Systemic Design Association and is truly changing the way that solutions are designed.
We invite you to come and discover how the Systemic Design Toolkit is driving a democratisation and transformation of the solutions design process for all stakeholders involved.
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'aMahmoud Bahgat
#Mahmoud_Bahgat
#Marketing_Club
Join us by WhatsApp to me 00966568654916
*اشترك في صفحة ال Marketing Club* عالفيسبوك
https://www.facebook.com/MarketingTipsPAGE/
*اشترك في جروب ال Marketing Club* عالفيسبوك
https://www.facebook.com/groups/837318003074869/
*Marketing Club Middle East*
25 Meetings in 6 Cities in 1 year & 2 months
Since October 2015
*We have 6 groups whatsapp*
*for almost 600 marketers*
From all middle east
*since 5 years*
& now 10 more groups
For Marketing Club Lovers as future Marketers
أهم حاجة الشروط
*Only marketers*
From all Industries
No students
*No sales*
*No hotels Reps*
*No restaurants Reps*
*No Travel Agents*
*No Advertising Agencies*
*Many have asked to Attend the Club*
((We Wish All can Attend,But Cant..))
*Criteria of Marketing Club Members*
•••••••••••••••••••••••••••••••••••••
For Better Harmony & Mind set.
*Must be only Marketer*
*Also Previous Marketing experience*
●Business Managers
●Country Manager,GM
●Directors, CEO
Are most welcomed to add Value to us.
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
Not Med Rep,
Not Key Account,
Not Product Specialist,
Not Sales Supervisor,
Not Sales Manager,
●●●●●●●●●●●●●●●●●●
But till you become a marketer
you can join other What'sApp group
*Marketing Lover Future Club Group*
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
For Conflict of Intrest
*Also Can't attend*
If Working in
*Marketing Services Provider*
=not *Hotel* Marketers
=not *Restaurant* Marketers
=not *Advertising* Marketer
=not *Event Manager*
=not *Market Researcher*.
■■■■■■■■■■■■■■■■
■■■■■■■■■■■■■■■■
*this Club for Only Marketers*
Very Soon we will have
*Business Leaders Club*
For Sales Managers & Directors
Will be Not for Markters
●●●●●●●●●●●●●●●●●●●●
■ *Only Marketers* ■
*& EPS Marketing Diploma*
●●●●●●●●●●●●●●●●●●●●
Confirm coming by Pvt WhatsApp
*To know the new Location*
*#Mahmoud_Bahgat*
00966568654916
*#Marketing_Club*
http://goo.gl/forms/RfskGzDslP
*اشترك بصفحة جمعية الصيادلة المصريين* عالفيسبوك
https://lnkd.in/fucnv_5
■ *Bahgat Facbook Page*
https://lnkd.in/fVAdubA
■ *Bahgat Linkedin*
https://lnkd.in/fvDQXuG
■ *Bahgat Twitter*
https://lnkd.in/fmNC72T
■ *Bahgat YouTube Channel*
https://www.Youtube.com /mahmoud bahgat
■ *Bahgat Instagram*
https://lnkd.in/fmWPXrY
■ *Bahgat SnapChat*
https://lnkd.in/f6GR-mR
*#Mahmoud_Bahgat*
*#Legendary_ADLAND*
www.TheLegendary.info
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
Using Data Integration Modelsfor Understanding Complex Social SystemsBruce Edmonds
Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).
The role of Information System is very significant in today’s competitive environment for the sake of protecting the core capability of any company. Information System helps the official stakeholders of the organization by providing them reliable updates and helps the industries where immediate updates are very crucial; some of these industries are travelling services, stock exchange, banking and the like. Almost all the companies are now investing in to Information System in order to reap the core benefits that it offers. However, these investments do not always end up be reaping benefits; risk is definitely involved in this case and ‘failures’ are unfortunately a part of this very field. Researchers have tried to come up with the major causes for these failures; even academicians have put in their efforts to do so. However, none of them has been able to resolve this complex mystery.
Not sure how to do this case analysis please help me do it!1.Are t.pdfamitbagga0808
Not sure how to do this case analysis please help me do it!
1.Are the regions similar?
2.What would be a good forecasting method to use?
3.Are there more advanced methods that might be considered?
Here is the data
https://docs.google.com/spreadsheets/d/1LYZHLx9f0Av9FJ6NLfULMU61cZPV3voqrVzbJNfV
jKk/edit?usp=sharing
Solution
Most people view the world as consisting of a large number of alternatives. Futures research
evolved as a way of examining the alternative futures and identifying the most probable.
Forecasting is designed to help decision making and planning in the present.
Forecasts empower people because their use implies that we can modify variables now to alter
(or be prepared for) the future. A prediction is an invitation to introduce change into a system.
There are several assumptions about forecasting:
1. There is no way to state what the future will be with complete certainty. Regardless of the
methods that we use there will always be an element of uncertainty until the forecast horizon has
come to pass.
2. There will always be blind spots in forecasts. We cannot, for example, forecast completely
new technologies for which there are no existing paradigms.
3. Providing forecasts to policy-makers will help them formulate social policy. The new social
policy, in turn, will affect the future, thus changing the accuracy of the forecast.
Many scholars have proposed a variety of ways to categorize forecasting methodologies. The
following classification is a modification of the schema developed by Gordon over two decades
ago:
Genius forecasting - This method is based on a combination of intuition, insight, and luck.
Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts
are based exclusively on intuition. Science fiction writers have sometimes described new
technologies with uncanny accuracy.
There are many examples where men and women have been remarkable successful at predicting
the future. There are also many examples of wrong forecasts. The weakness in genius forecasting
is that its impossible to recognize a good forecast until the forecast has come to pass.
Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream
science generally ignores this fact because the implications are simply to difficult to accept. Our
current understanding of reality is not adequate to explain this phenomena.
Trend extrapolation - These methods examine trends and cycles in historical data, and then use
mathematical techniques to extrapolate to the future. The assumption of all these techniques is
that the forces responsible for creating the past, will continue to operate in the future. This is
often a valid assumption when forecasting short term horizons, but it falls short when creating
medium and long term forecasts. The further out we attempt to forecast, the less certain we
become of the forecast.
The stability of the environment is the key factor in determining whether tren.
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
A talk at the workshop on "Agent-Based Models in Philosophy: Prospects and Limitations", Rurh University, Bochum, Germany
Abstract:
ABMs (like other kinds of model) can be used in a purely abstract way, as a kind of thought experiment - a way of thinking about some aspect of the world that is too complicated to hold in our mind (in all its detail). In this way it both informs and complements discursive thought. However there is another set of uses for ABMs - empirical uses - where the mapping between the model and sets of observation-derived data are crucial. For these uses, one has to (a) use the mapping to get from some data to the model (b) use the model for some inference and (c) use the mapping again back to data. This includes both predictive and explanatory uses of ABMs. These are easily distinguishable from abstact uses becuase there is a fixed and well-defined relationship between the model and the data, this is not flexible on a case by case basis. In these cases the reliability comes from the composite (a)-(b)-(c) mapping, so that simplifying step (b) can be counterproductive if that means weakening steps (a) and (c) because it is the strength of the overall chain that is important. Taking the use of models in quantum mechanics as an example, one can see that sometimes the evolution of the formal models driven by empirical adequacy can be more important than the attendent abstract models used to get a feel for what is happening. Although using ABM's for empirical purposes is more challenging than for purely abstract purposes, they are being increasingly used for empirical explanation rather than thought experiments, and there is no reason to suppose that robust empirical adequacy is unachievable.
More Related Content
Similar to Finding out what could go wrong before it does – Modelling Risk and Uncertainty
Mixing ABM and policy...what could possibly go wrong?Bruce Edmonds
Invited talk at 19th International Workshop on Multi-Agent Based Simulation at Stockholm on 14th July 2018.
Mixing ABM and Policy ... what could possibly go wrong?
This talk looks at a number of ways in which using ABM in the context of influencing policy can go wrong: during model construction, with model application and other.
It is related to the book chapter:
Aodha, L. and Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity - a handbook, 2nd edition. Springer, 801-822.
Socio-Ecological Simulation - a risk-assessment approachBruce Edmonds
An invited talk in Tromsoe, 5 June 2018.
Both social and ecological systems are complex, but when they combine (as when human societies farm/hunt) there is a double complexity. This complexity means it is infeasible to predict the outcome of their interaction and unwise to rely on any prediction. An alternative approach is to use complex simulations to try and discover some possible ways that such systems can go wrong. This can reveal risks that other approaches might miss, due to the fact that more of the complexity is included within the model. Once a risk is identified then measures to monitor its emergence can be implemented, allowing the earliest possible warning of this. An example of this approach applied to a fisheries ecosystem is described.
Policy Making using Modelling in a Complex worldBruce Edmonds
A talk given at the CECAN workshop, London July 2016
Abstract:
The consequences of complexity in the real world are discussed together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are fundamentally unpredictable by nature, especially when in the presence of structural change (transitions). This implies consequences for the way we model, but also for the way models are used in the policy process.
I discuss the problems arising from a too narrow focus on quantification in managing complex systems, in particular those of optimisation. I criticise some of the approaches that ignore these difficulties and pretend to approximately forecast using the impact of policy options using over-simple models. However, lack of predictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from "Complexity Science" can help with such management. Managing complex systems requires a good understanding of the dynamics of the system in question - to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent based simulation will be discussed as a tool that is suitable for this task, especially in conjunction with model-informed data visualisation.
Towards Integrating Everything (well at least: ABM, data-mining, qual&quant d...Bruce Edmonds
A talk given at the SKIN3 workshop in Budapest, May 2014 (http://cress.soc.surrey.ac.uk/SKIN/events/third-skin-workshop)
Innovation or other policy-orientated research has tended to take one of two strategies: (a) work with high-level abstractions of macro-level variables or (b) focus on micro-level aspects/areas with simpler mechanisms. Whilst (a) may provide some comfort in the form of forecasts, these are almost useless for policy since they can only be relied upon if nothing much has changed. Although approach (b) may produce some interesting studies which show how complex even small aspects of the involved processes are, with maybe interesting emergent effects, it provides only a small part of the overall picture and little to guide decision making.
Rather, I (with others) suggest a different approach. Instead of aiming to produce some kind of "adequate" theory (usually in the form of a model along with its interpretation), that instead we aim at integrating different kinds of evidence and find the best ways to present these to policy makers in order to help policy-makers 'drive' by providing views of what is happening. Thus (1) utilising the greatest possible range of evidence and (2) providing rich, relevant but synthetic views of this evidence to the policy makers. Any projections should be 'possibilistic' rather than 'probabilistic' - showing the different ways in which social processes might unfold, and help inform the analysis of risks. The talk looks at some of the ways in which this might be done, to integrate micro-level narrative data, time-series data, survey data, network data, big data using a variety of techniques. In this view, models do not disappear, but rather have a different purpose and hence be developed and checked differently.
This shift will involve a change in attitude and approach from both researchers and those in the policy world. Researchers will have to give up the playing for general or abstract theory, satisfying themselves with more gentle and incremental abstraction, whilst also accepting and working with a greater variety of kinds of evidence. They will also have to stop 'conning' the policy world with forecasts, and refuse to provide these as more dangerous than helpful. The policy world will have to stop looking for a magic 'crutch' that will reduce uncertainty (or provide justification for chosen policies) and move towards greater openness with both data and models.
BA and Beyond 19 Sponsor spotlight - Namahn - Beating complexity with complexityBA and Beyond
It’s a complex world full of complex problems- organisational change, the income inequality gap and digital transformation just to name a few.
The conventional way of combatting complexity to solve problems no longer works.
The great minds of Systemic Design have come together to create a unique and innovative toolkit designed to embrace complexity and change the way that we design solutions.
The first of its kind, the toolkit is based on academic research and human-centred design expertise. It is also the first to be endorsed by the Systemic Design Association and is truly changing the way that solutions are designed.
We invite you to come and discover how the Systemic Design Toolkit is driving a democratisation and transformation of the solutions design process for all stakeholders involved.
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'aMahmoud Bahgat
#Mahmoud_Bahgat
#Marketing_Club
Join us by WhatsApp to me 00966568654916
*اشترك في صفحة ال Marketing Club* عالفيسبوك
https://www.facebook.com/MarketingTipsPAGE/
*اشترك في جروب ال Marketing Club* عالفيسبوك
https://www.facebook.com/groups/837318003074869/
*Marketing Club Middle East*
25 Meetings in 6 Cities in 1 year & 2 months
Since October 2015
*We have 6 groups whatsapp*
*for almost 600 marketers*
From all middle east
*since 5 years*
& now 10 more groups
For Marketing Club Lovers as future Marketers
أهم حاجة الشروط
*Only marketers*
From all Industries
No students
*No sales*
*No hotels Reps*
*No restaurants Reps*
*No Travel Agents*
*No Advertising Agencies*
*Many have asked to Attend the Club*
((We Wish All can Attend,But Cant..))
*Criteria of Marketing Club Members*
•••••••••••••••••••••••••••••••••••••
For Better Harmony & Mind set.
*Must be only Marketer*
*Also Previous Marketing experience*
●Business Managers
●Country Manager,GM
●Directors, CEO
Are most welcomed to add Value to us.
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
Not Med Rep,
Not Key Account,
Not Product Specialist,
Not Sales Supervisor,
Not Sales Manager,
●●●●●●●●●●●●●●●●●●
But till you become a marketer
you can join other What'sApp group
*Marketing Lover Future Club Group*
■■■■■■■■■■■■■■■■
《 *Unmatched Criteria*》
For Conflict of Intrest
*Also Can't attend*
If Working in
*Marketing Services Provider*
=not *Hotel* Marketers
=not *Restaurant* Marketers
=not *Advertising* Marketer
=not *Event Manager*
=not *Market Researcher*.
■■■■■■■■■■■■■■■■
■■■■■■■■■■■■■■■■
*this Club for Only Marketers*
Very Soon we will have
*Business Leaders Club*
For Sales Managers & Directors
Will be Not for Markters
●●●●●●●●●●●●●●●●●●●●
■ *Only Marketers* ■
*& EPS Marketing Diploma*
●●●●●●●●●●●●●●●●●●●●
Confirm coming by Pvt WhatsApp
*To know the new Location*
*#Mahmoud_Bahgat*
00966568654916
*#Marketing_Club*
http://goo.gl/forms/RfskGzDslP
*اشترك بصفحة جمعية الصيادلة المصريين* عالفيسبوك
https://lnkd.in/fucnv_5
■ *Bahgat Facbook Page*
https://lnkd.in/fVAdubA
■ *Bahgat Linkedin*
https://lnkd.in/fvDQXuG
■ *Bahgat Twitter*
https://lnkd.in/fmNC72T
■ *Bahgat YouTube Channel*
https://www.Youtube.com /mahmoud bahgat
■ *Bahgat Instagram*
https://lnkd.in/fmWPXrY
■ *Bahgat SnapChat*
https://lnkd.in/f6GR-mR
*#Mahmoud_Bahgat*
*#Legendary_ADLAND*
www.TheLegendary.info
Some supporting slides on modelling purposes and pitfalls when using ABM in policy contexts to accompany discussion on Modelling Pitfalls at the ESSA Summer School, Aberdeen, June 2019
Using Data Integration Modelsfor Understanding Complex Social SystemsBruce Edmonds
Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).
The role of Information System is very significant in today’s competitive environment for the sake of protecting the core capability of any company. Information System helps the official stakeholders of the organization by providing them reliable updates and helps the industries where immediate updates are very crucial; some of these industries are travelling services, stock exchange, banking and the like. Almost all the companies are now investing in to Information System in order to reap the core benefits that it offers. However, these investments do not always end up be reaping benefits; risk is definitely involved in this case and ‘failures’ are unfortunately a part of this very field. Researchers have tried to come up with the major causes for these failures; even academicians have put in their efforts to do so. However, none of them has been able to resolve this complex mystery.
Not sure how to do this case analysis please help me do it!1.Are t.pdfamitbagga0808
Not sure how to do this case analysis please help me do it!
1.Are the regions similar?
2.What would be a good forecasting method to use?
3.Are there more advanced methods that might be considered?
Here is the data
https://docs.google.com/spreadsheets/d/1LYZHLx9f0Av9FJ6NLfULMU61cZPV3voqrVzbJNfV
jKk/edit?usp=sharing
Solution
Most people view the world as consisting of a large number of alternatives. Futures research
evolved as a way of examining the alternative futures and identifying the most probable.
Forecasting is designed to help decision making and planning in the present.
Forecasts empower people because their use implies that we can modify variables now to alter
(or be prepared for) the future. A prediction is an invitation to introduce change into a system.
There are several assumptions about forecasting:
1. There is no way to state what the future will be with complete certainty. Regardless of the
methods that we use there will always be an element of uncertainty until the forecast horizon has
come to pass.
2. There will always be blind spots in forecasts. We cannot, for example, forecast completely
new technologies for which there are no existing paradigms.
3. Providing forecasts to policy-makers will help them formulate social policy. The new social
policy, in turn, will affect the future, thus changing the accuracy of the forecast.
Many scholars have proposed a variety of ways to categorize forecasting methodologies. The
following classification is a modification of the schema developed by Gordon over two decades
ago:
Genius forecasting - This method is based on a combination of intuition, insight, and luck.
Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts
are based exclusively on intuition. Science fiction writers have sometimes described new
technologies with uncanny accuracy.
There are many examples where men and women have been remarkable successful at predicting
the future. There are also many examples of wrong forecasts. The weakness in genius forecasting
is that its impossible to recognize a good forecast until the forecast has come to pass.
Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream
science generally ignores this fact because the implications are simply to difficult to accept. Our
current understanding of reality is not adequate to explain this phenomena.
Trend extrapolation - These methods examine trends and cycles in historical data, and then use
mathematical techniques to extrapolate to the future. The assumption of all these techniques is
that the forces responsible for creating the past, will continue to operate in the future. This is
often a valid assumption when forecasting short term horizons, but it falls short when creating
medium and long term forecasts. The further out we attempt to forecast, the less certain we
become of the forecast.
The stability of the environment is the key factor in determining whether tren.
Similar to Finding out what could go wrong before it does – Modelling Risk and Uncertainty (20)
Staging Model Abstraction – an example about political participationBruce Edmonds
A presentation at the workshop on ABM and Theory (From Cases to General Principles), Hannover, July 2019
This reports on work where we started with a complex, but evidence driven model, and then modelled that model sto understand and abstract from it. As reported in the paper:
Lafuerza LF, Dyson L, Edmonds B, McKane AJ (2016) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261
A talk at the workshop on "Agent-Based Models in Philosophy: Prospects and Limitations", Rurh University, Bochum, Germany
Abstract:
ABMs (like other kinds of model) can be used in a purely abstract way, as a kind of thought experiment - a way of thinking about some aspect of the world that is too complicated to hold in our mind (in all its detail). In this way it both informs and complements discursive thought. However there is another set of uses for ABMs - empirical uses - where the mapping between the model and sets of observation-derived data are crucial. For these uses, one has to (a) use the mapping to get from some data to the model (b) use the model for some inference and (c) use the mapping again back to data. This includes both predictive and explanatory uses of ABMs. These are easily distinguishable from abstact uses becuase there is a fixed and well-defined relationship between the model and the data, this is not flexible on a case by case basis. In these cases the reliability comes from the composite (a)-(b)-(c) mapping, so that simplifying step (b) can be counterproductive if that means weakening steps (a) and (c) because it is the strength of the overall chain that is important. Taking the use of models in quantum mechanics as an example, one can see that sometimes the evolution of the formal models driven by empirical adequacy can be more important than the attendent abstract models used to get a feel for what is happening. Although using ABM's for empirical purposes is more challenging than for purely abstract purposes, they are being increasingly used for empirical explanation rather than thought experiments, and there is no reason to suppose that robust empirical adequacy is unachievable.
Social Context
An invited talk at the 2018 Surrey Sociology Conference, Barnett Hill, Surrey, November 2018.
Although there is much evidence that context is crucial to much human cognition and social behaviour, it remains a difficult area to research. In much social science research it is either by-passed or ignored. In some qualitative research context is almost deified with any level of generalisation across contexts being left to the reader. At the other extreme, some qualitative research restricts itself to patterns that are generally detectable - that is the patterns that are left when one aggregates over many different contexts. Context is often used as a 'dustbin concept' to which otherwise unexplained variation is attributed.
This talk looks at some of the ways social context might be actively represented, understood and researched. Firstly the ideas of cognitive then social context are distinguished. Then some possible approaches to researching this are discussed, including: agent-based simulation, a context-sensitive analysis of narrative data and machine learning.
Using agent-based simulation for socio-ecological uncertainty analysisBruce Edmonds
A talk given in the MMU Big Data Centrem, 30th October 2018.
Both social and ecological systems can be highly complex, but the interaction between these two worlds - a socio-ecological system (SES) - can add even greater levels. However, the maintenance of SES are vital to our well being and the health of the planet. We do not know how such systems work in practice and we lack good data about them (especially the ecological side) so predicting the effect of any particular policy is infeasible. Here we present an approach which tries to understand some of the ways in which SES may go wrong, but constructing different complex simulation models and analysing the emergent outcomes. These, in silico, examples can allow for the institution of targeted data gathering instruments that give the earliest possible warning of deleterious outcomes, and thus allow for timely remedial responses. An example of this approach applied to fisheries is described.
How social simulation could help social science deal with contextBruce Edmonds
An invited plenary at Social Simluation 2018, Stockholm.
This points out how context-sensitivity is fundamental to much human social behaviour, but largely bypassed or ignored in social science. I more formal social science, it is usual to assume or fit universal models, even if this covers a lot of different contexts. In qualitative social science context is almost deified, and any generalisation across contexts is passed on to those that learn from it. Agent-based modelling allows for context-sensitive models to be developed and hence the role of context explored and better understood. The talk discussed a framework for analysing narrative text using the Context-Scope-Narrative-Elements (CSNE) framework. It also illustrates a cognitive model that allows for context-dependent knowledge to be implemented wthin an agent in a simulation. The talk ends with a plea to avoid uncecessary or premature summarisation (using averages etc.).
Agent-based modelling,laboratory experiments,and observation in the wildBruce Edmonds
An invited talk at the workshop on "Social complexity and laboratory experiments – testing assumptions and predictions of social simulation models with experiments" at Social Simulation 2018, Stockholm
Culture trumps ethnicity!– Intra-generational cultural evolution and ethnoce...Bruce Edmonds
Essential to understanding the impact of in-group bias on society is the micro-macro link and the complex dynamics involved. Agent-based modelling (ABM) is the only technique that can formally represent this and thus allow for the more rigorous exploration of possi-ble processes and their comparison with observed social phenomena. This talk discusses these issues, providing some examples of some relevant ABMs.
A talk given at the BIGSSS summer school on conflict, Bremen, Jul/Aug 2018.
An Introduction to Agent-Based ModellingBruce Edmonds
An introduction to the technique with two example models of in-group bias and voter turnout.
An invited talk at the BIGSSS Summer Schools in Computational Social Science, at the Jacobs Bremen University, July 2018.
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
Models are a tool, not a picture of reality. There are many different uses for models. The intended use of a model - its 'purpose' - affects how it is judged, checked and developed. Much confusion and bad practice in modelling can be attributed to not clearly identifying the intended 'purpose' for a model. Neo-classical Economics is used to illustrate some of these confusions. In some (but not all) uses the model stands in for a theory (at least key aspects of it), but this can happen in different ways and at different levels of abstraction. The talk looks at some of these different ways and advocates a staged, inductive methodology for theory development instead of one that jumps to high generality and simple models which confuse different uses.
A talk given at the Workshop on "From Cases To General Principles - Theory Development Through Agent-Based Modeling" see http://abm-theory.org
A talk at the workshop on "Thinking toys (or games) for commoning, Basel, 5/6 April, Switzerland.
This describes a simple model of anonymous donation of resources, with minimal group structuring.
Am open-access paper on this model is at: http://cfpm.org/discussionpapers/152
The model can be freely downloaded from:
http://openABM.org/model/4744
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
The Post-Truth Drift in Social SimulationBruce Edmonds
A talk at the Social Simulation Conference, Dublin, September 2017.
Abstract
The paper identifies a danger in the field of social simulation a danger of using weasel words to give a false impression to the world about the achievements of our field. Whether this is intentional or unintentional, the effect might be to damage the reputation of the field and impair its development. At the root of this is a need for brutal honesty and openness, something that can be personally difficult and that needs social support. The paper considers some of the subtle ways that this kind of post-truth drift might occur, including: confusion/conflation of modelling purpose, wishing to justify pragmatic limitations in our work, falling back to unvalidated theory, confusing using a model for a way of looking at the world for something more reliable, and seeking protection from critique in vagueness. It calls on social simulation researchers to firmly reject such a drift.
Drilling down below opinions: how co-evolving beliefs and social structure mi...Bruce Edmonds
A talk at ODCD2017, Jocob's University, Bremen, July 2017. (http://odcd2017.user.jacobs-university.de/)
The talk looks at an alternative to "linear" models which deal with a euclidean space of opinions (usually a 1D space). This is a model of belief change, where both social influence and internal consistency of beliefs co-evolve with social structure. Thus this goes beyond most opinion dynamics models in a number of ways: (a) it deals with beliefs that may underlie measured opinions (b) the internal coherency among sets of beliefs is important as well as social influence (c) the social structure co-evolves with belief change and (d) the social structures are complex and continually dynamic. The internal consistency of beliefs is based on Thagard's theory of explanatory coherence, which has some empirical support. The model seems to display some of the tensions and processes that are observed in politics, for example: the tension between moderating views so as to connect with the public vs. reinforcing the in-group coherency. It displays a dynamic that can reflect a number of different courses including those that result turning points in opinions.
A talk at the ESSA Silico Summer School in Wageningen, June 2017. It looks at some of the different purposes for a simulation model, and how complicated one should make one's model
Modelling Innovation – some options from probabilistic to radicalBruce Edmonds
A talk on the various kinds of innovation based on Margret Boden's types of creativity . Given at the European Academy, Ahrweiler, Germany 31st May 2017.
Co-developing beliefs and social influence networksBruce Edmonds
Argues that many social phenomena needs ABM models with both cognitive and social change co-developing
Presented at the AISB workshop in Bath, April 2017 on "The power of Immergence...". See last slide for details of where to get the paper and the model
An invited talk given at the Institute for Research into Superdiversity (IRIS), University of Brimingham, 31st Jan 2017
Abstract:
A simulation to illustrate how the complex patterns of cultural and genetic signals might combine to define what we mean by "groups" of people is presented. In this model both (a) how each individual might define their "in group" and (b) how each individual behaves to others in 'in' or 'out' groups can evolve over time. Thus groups are not something that is precisely defined but is something that emerges in the simulation. The point is to illustrate the power of simulation techniques to explore such processes in a non-prescriptive way that takes the micro-macro distinction seriously and represents them within complex simulations. In the particular simulation presented, groups defined by culture strongly emerge as dominant and ethnically defined groups only occur when they are also culturally defined.
A Model of Social and Cognitive CoherenceBruce Edmonds
An inbvited talk at the Workshop on Coherence -Based Approaches to Decision-Making, Cognition and Communication, Berlin July 2016
Human cognition can be usefully understood as a primarily social set of abilities - its survival benefit is from our ability to social organise and hence inhabit a variety of niches. From this point of view any ability makes more sense when put into a social context. This includes our innate ability to judge candidate beliefs in terms of their coherency with our existing beliefs and goals. However studying cognition in its social context implies high complexity, for this reason I describe an agent-based model of coherency based belief within a dynamic network of individuals. Here beliefs might be copied (or discarded) by an individual based upon the change in coherence it causes with its other beliefs, but also that an individual will change their social connections based upon the the coherence of their beliefs with those they socially interact with.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
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Finding out what could go wrong before it does – Modelling Risk and Uncertainty
1. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 1
Finding out what could go wrong before it does
– Modelling Risk and Uncertainty
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
2. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 2
Classic Policy Modelling
Essential steps:
1. Decide on KPIs of policy success
2. List candidate policies
3. Predict impact of policies: cost and KPIs
4. Choose best policy
Sometimes this is embedded within a repeated
cycle of:
a) Decide on a policy (using steps 2-4 above)
b) Implement it
c) Evaluate the policy
3. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 3
Statistical Models
Approach:
1. Regress KPIs on known outputs
2. Choose inputs that maximise KPIs
3. Hence choose the policy that might most closely implement
those inputs
• Assumes generic fixed relationship – average
success
• Straightforward to do
• Requires enough data between KPIs and inputs
• Candidate policies and regressed inputs may not be
obviously relatable
• Not customisable to particular situations
4. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 4
Micro-Simulation Models
Approach:
1. Divide up population in to areas/groups
2. Choose simple statistical or other model for reaction
3. For each area/group regress/adjust model for their
own data
4. Maybe add some flows between areas/groups
5. Aggregate over areas/groups for overall assessment
• Requires details data for each area/group
• Good for heterogeneity of groups/areas
• Does not work so well when lots of interaction
between groups
5. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 5
Computable General Equilibrium
Models
Approach:
1. Construct a simplified economic model of situation
with and without chosen policy
2. Calculate equilibrium without policy
3. Calculate equilibrium with policy
4. Compare the two equilibria and see if this represents
an improvement and how much of one
• Only simple models are calculable
• Uses strong economic assumptions
• The equilibrium is only one restricted and long-
term aspect of the outcomes
• Does not have a good predictive record
6. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 6
System Dynamic Models
Approach:
1. Build relationship between key variables using flow
and storage approach (maybe in a participatory way)
2. Add in equations and delays
3. Run simulated system with probably inputs
4. Evaluate the results somehow
• Good for dynamics with delayed feedback
• Does not deal with heterogeneity of actors
• ‘Touchy-feely’ judgment of outcomes
• Can look more real that evidence proves
• Not good at predicting outcome values
7. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 7
Simulation Models
Approach:
1. Build a simulation reflecting how parts of system relate
2. Adjust parameters reflecting particular situation/data
3. Check simulation by running for known situation where
outcomes and data is known (validation)
4. Produce different variations of simulation to reflect each
policy to be tested
5. Run each variation many times and measure the outcomes
• Simulation only as strong as knowledge of system
• Might have many unknown parameters
• Never enough data to sufficiently validate
• Policies can be directly implemented
• Outcomes assessed in many different ways
8. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 8
Some modelling tensions
precision (model not vague)
generality
of scope (works for
different cases)
Lack of error (accuracy of results)
realism
(reflects
knowledge of
processes)
Economic Models
Scenarios
Agent-based
models
Stats/regression
models
Reality
Wanted for
policy decisions
9. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 9
Problem 1: System Complexity
• There is no guarantee that a simple model will be
adequate to representing complex
social/ecological/economic/technical systems
• How the parts and actors interact and react might
be crucial to the outcomes (e.g. financial markets)
• We may not know which parts of the system are
crucial to the outcomes
• We may not fully understand how the parts
interact and react
• System and model are both too complex to fully
explore and understand in time available
10. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 10
Problems 2&3: Error and Uncertainty
• The values of many key parameters might be
unknown or only approximately known
• Data might be patchy and of poor quality
• Tiny changes in key factors or parameters might
have huge consequences for outcomes (the
‘butterfly effect’)
• Levels of error may be amplified by the system
(as in automated trading in financial markets)
• There may be processes that we do not even
know are important to the outcomes
11. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 11
Problem 4: Structural Change
• System evolves due to internal dynamics
• For example, innovations might occur
• System might have several very different
behavioural ‘phases’ (e.g. bull and bear markets)
which it shifts between
• The rules of the system might change rapidly…
• ...and well before any equilibrium is reached
• Rule-change might be linked to system state
• Different parts of the system might change in
different ways
12. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 12
Prediction
• Given all these difficulties for many situations,
prediction is not only infeasible…
• ...but suggesting you can predict is dishonest
• and may give false comfort (e.g. Newfoundland
Cod Fisheries Collapse or 2007/8 financial crash)
• Most techniques only work in two cases, where:
1. There is lots of experience/data over many previous
episodes/cases
2. Nothing much changes (tomorrow similar to today)
• Often even approximate or probabilistic
prediction is infeasible and unhelpful
13. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 13
The key question….
How does one manage a system or
situation that is too complex to predict?
14. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 14
Lessons from robotics: Part I
Robotics in the 70s and 80s tried to (iteratively):
1. build a map of its situation (i.e. a predictive
model)
2. use this model to plan its best action
3. then try to do this action
4. check it was doing OK go back to (1)
But this did not work in any realistic situation:
• It was far too slow to react to its world
• to make useable predictions it had to make too
many dodgy assumptions about its world
15. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 15
Lessons from robotics: Part II
Rodney Brooks (1991) Intelligence without
representation. Artificial Intelligence, 47:139–160
A different approach:
1. Sense the world in rich fast ways
2. React to it quickly
3. Use a variety of levels of reaction
a. low simple reactive strategies
b. switched by progressively higher ones
Do not try to predict the world, but react to it quickly
This worked much better.
16. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 16
Lessons from Weather Forecasting
• Taking measurements at a few places and trying
to predict what will happen based on simple
models based on averages does not work well
• Understanding the weather improved with very
detailed simulations fed by rich and
comprehensive sensing of the system
• Even then they recognize that there are more
than one possibilities concerning the outcomes
(using ensembles of specific outcomes)
• If these indicate a risk of severe weather they
issue a warning so mitigating measures can be
taken
17. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 17
Lessons from Radiation Levels
• The human body is a very complex system
• It has long been known that too much radiation
can cause severe illness or death in humans
• In the 30s & 40s it was assumed there was a
“safe” level of radiation
• However it was later discovered that any level of
radiation carried a risk of illness
• Including naturally occurring levels
• Although an increase in radiation might not seem
to affect many people, it did result in more
illnesses in some
18. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 18
Socio-Ecological Systems
• Are the combination of human society embedded
within an ecological system (SES)
• Many social and ecological systems are far too
complex to predict
• Their combination is doubly complex
• E.g. fisheries, deforestation, species extinctions
• Yet we still basically use the 1970s robotics
“predict and plan” approach to these…
• …as if we can plan optimum policies by
estimating/projecting future impact
19. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 19
Why simple models won’t work
• Simpler models do not necessarily get things
“roughly” right
• Simpler models are not more general
• They can also be very deceptive – especially with
regards to complex ways things can go wrong
• In complex systems the detailed interactions can
take outcomes ‘far from equilibrium’ and far from
average behaviour
• Sometimes, with complex systems, a simple
model that relies on strong assumptions can be
far worse than having no models at all
20. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 20
A Cautionary Tale
• On the 2nd July 1992 Canada’s fisheries minister,
placed a moratorium on all cod fishing off
Newfoundland. That day 30,000 people lost their jobs.
• Scientists and the fisheries department throughout
much of the 1980s estimated a 15% annual rate of
growth in the stock – (figures that were consistently
disputed by inshore fishermen).
• The subsequent Harris Report (1992) said (among
many other things) that: “..scientists, lulled by false
data signals and… overconfident of the validity of
their predictions, failed to recognize the statistical
inadequacies in … [their] model[s] and failed to …
recognize the high risk involved with state-of-stock
advice based on … unreliable data series.”
21. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 21
What had gone wrong?
• “… the idea of a strongly rebuilding Northern cod
stock that was so powerful that it …[was]... read
back… through analytical models built upon
necessary but hypothetical assumptions about
population and ecosystem dynamics. Further, those
models required considerable subjective judgement
as to the choice of weighting of the input variables”
(Finlayson 1994, p.13)
• Finlayson concluded that the social dynamics
between scientists and managers were at play
• Scientists adapting to the wishes and worldview of
managers, managers gaining confidence in their
approach from the apparent support of science
22. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 22
Example 1: Fishing!
• …is a dynamic, spatial, individual-based ecological model
that has some of the complexity, adaptability and fragility
of observed ecological systems with emergent outcomes
• It evolves complex, local food webs, endogenous shocks
from invasive species, is adaptive but unpredictable as to
the eventual outcomes
• Into this the impact of humans can be imposed or even
agents representing humans ‘injected’ into the simulation
• The outcomes can be then analysed at a variety of levels
over long time scales, and under different scenarios
• Paper: Edmonds, B. (in press) A Socio-Ecological Test
Bed. Ecology & Complexity.
• Full details and code at: http://openabm.org/model/4204
23. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 23
In this version
• Plants and higher order entities (fish)
distinguished (no photosynthesizing herbivores!)
• First a rich competing plant ecology is evolved
• Then single fish injected until fish take hold and
evolve until there is an ecology of many fish
species, run for a bit to allow ‘transients’ to go
• This state then frozen and saved
• From this point different ‘fishing’ polices
implemented and the simulations then run
• with the outcomes then analysed
24. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 24
The Model
• A wrapped 2D grid of
well-mixed patches with:
– energy (transient)
– bit string of characteristics
• Organisms represented
individually with its own
characteristics,
including:
– bit string of characteristics
– energy
– position
A well-mixed
patch
Each
individual
represented
separately
Slow
random rate
of migration
between
patches
25. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 25
Model sequence each simulation tick
1. Input energy equally divided between patches.
2. Death. A life tax is subtracted, some die, age incremented
3. Initial seeding. until a viable is established, random new individual
4. Energy extraction from patch. energy divided among the
individuals there with positive score when its bit-string is evaluated
against patch
5. Predation. each individual is randomly paired with a number of
others on the patch, if dominate them, get a % of their energy, other
removed
6. Maximum Store. energy above a maximum level is discarded.
7. Birth. Those with energy > “reproduce-level” gives birth to a new
entity with the same bit-string as itself, with a probability of mutation,
Child has an energy of 1, taken from the parent.
8. Migration. randomly individuals move to one of 4 neighbours
9. Statistics. Various statistics are calculated.
26. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 26
First, evolve a rich mixed ecology
Evolve and save a suitable complex ecology with
a balance of tropic layers (final state to the left
with log population scale)
Herbivores
Appear
First Successful
Plant
Simulation
“Frozen”
Carnivores
Appear
27. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 27
This version designed to test possible
outcomes of fishing policies
• Complex aquatic plant ecology evolved
• Herbivore fish injected into ecology and whole
system further evolved
• Once a complex ecology with higher-order
predators then system is fixed as starting point
• Different extraction (i.e. fishing) policies can be
enacted on top of this system:
– How much fish is extracted each time (either absolute
numbers or as a proportion of existing numbers)
– Where uniformly at random or patch-by-patch
– How many ‘reserves’ are kept
– Is there a minimum stock level below which no fishing
28. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 28
Demonstration of the basic model
29. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 29
Typical Harvest Shape (last 100 ticks) for
different catch levels over 20 different runs
Catch level (per tick)
ProportionofMaximum
30. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 30
Decide in your groups
1. Amount of fish extraction (quota) per tick, either:
• Absolute number (0-200)
• Percentage of existing stock (0-100%)
2. The way fish is extracted, either:
• Randomly over whole grid
• Random patch chosen and fished, then next until
quota for tick is reached
3. How many patches will be kept as reserves (not
fished)
4. When to start fishing (0-999 ticks)
31. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 31
Total Extinction Prob. & Av. Total Harvest
(last 100 ticks) for different catch levels
Catch level (per tick)
ProportionofMaximum
32. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 32
Num Fish (all species, 20 runs) – catch
level 25
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992
33. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 33
Num Fish (all species, 20 runs) – catch
level 35
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992
Catch target=30
34. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 34
Num Fish (all species, 20 runs) – catch
level 50
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992
35. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 35
Average (over 20 runs) of fish at end of 5000
simulation ticks
0
1000
2000
3000
4000
5000
0 20 40 60 80 100
Number Fish for Different Catch Levels
36. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 36
Average (over 20 runs) of numbers of fish
species at end of 5000 simulation ticks
0
20
40
60
80
100
120
140
0 20 40 60 80 100
Num Fish Species with Catch Level
37. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 37
Average Number of Species vs. Catch
Level (from a different starting ecology)
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30 35 40
Num Species Fish
38. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 38
Average Number of Species, Catch=20
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"
39. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 39
Average Number of Species, Catch=30
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"
40. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 40
Average Number of Species, Catch=40
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"
41. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 41
A risk-analysis approach
1. Give up on estimating future impact or “safe”
levels of exploitation
2. Make simulation models that include more of the
observed complication and complex interactions
3. Run these lots of times with various scenarios to
discover some of the ways in which things can
go surprisingly wrong (or surprisingly right)
4. Put in place sensors/measures that would give
us the earliest possible warning that these might
be occurring in real life
5. React quickly if these warning emerge
42. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 42
Example 2: Social Influence and
Domestic Water Demand
• Produced for the Environment Agency/DEFRA
• Part of a bigger project to predict future domestic
water demand in the UK given some different
future politico-economic scenarios and climate
change
• The rest of the project were detailed statistical
models to do the prediction
• This model was to examine the assumptions and
look at the envelope of possibilities
• Joint work with Olivier Barthelemy and Scott Moss
43. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 43
Monthly Water Consumption
REL_CHNG
.88
.75
.63
.50
.38
.25
.13
0.00
-.13
-.25
-.38
-.50
20
10
0
Std.Dev = .17
Mean = .01
N = 81.00
44. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 44
Relative Change in Monthly
Consumption
Date
FEB
2001
SEP
2000
APR
2000
N
O
V
1999
JU
N
1999
JAN
1999
AU
G
1998
M
AR
1998
O
C
T
1997
M
AY
1997
D
EC
1996
JU
L
1996
FEB
1996
SEP
1995
APR
1995
N
O
V
1994
JU
N
1994
REL_CHNG
1.0
.8
.6
.4
.2
-.0
-.2
-.4
-.6
45. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 45
Purpose of the SI&DWD Model
• Not long-term prediction
• But to begin to understand the relationship of
socially-influenced consumer behaviour to
patterns of water demand
• By producing a representational agent model
amenable to fine-grained criticism
• And hence to suggest possible interactions
• So that these can be investigated/confirmed
• And this loop iterated
46. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 46
Model Structure - Overall Structure
•Activity
•Frequency
•Volume
Households
Policy
Agent
•Temperature
•Rainfall
•Sunshine
Ground
Aggregate Demand
47. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 47
Model Structure - Microcomponents
• Each household has a variable number of micro-
components (power showers etc.): bath
other_garden_watering shower hand_dishwashing
washing_machine sprinkler clothes_hand_washing
hand_dishwashing toilets sprinkler power_shower
• Actions are expressed by the frequency and
volume of use of each microcomponent
• AVF distribution in model calibrated by data from
the Three Valleys
48. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 48
Model Structure - Household
Distribution
• Households distributed randomly on a grid
• Each household can copy from a set of
neighbours (currently those up to 4 units up, down
left and right from them)
• They decide which is the neighbour most similar
to themselves – this is the one they are most
likely to copy
• Depending on their evaluation of actions they
might adopt that neighbour’s actions
49. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 49
An Example Social Structure
- Global Biased
- Locally Biased
- Self Biased
50. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 50
Household Behaviour - Endorsements
• Action Endorsements: recentAction neighbourhoodSourced
selfSourced globallySourced newAppliance
bestEndorsedNeighbourSourced
• 3 Weights moderate effective strengths of
neighbourhoodSourced selfSourced globallySourced
endorsements and hence the bias of households
• Can be characterised as 3 types of households
influenced in different ways: global-;
neighbourhood-; and self-sourced depending on
the dominant weight
51. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 51
History of a particular action
from one agent’s point of view
Month 1: used, endorsed as self sourced
Month 2: endorsed as recent (from personal use) and neighbour
sourced (used by agent 27) and self sourced (remembered)
Month 3: endorsed as recent (from personal use) and neighbour
sourced (agent 27 in month 2).
Month 4: endorsed as neighbour sourced twice, used by agents 26 and
27 in month 3, also recent
Month 5: endorsed as neighbour sourced (agent 26 in month 4), also
recent
Month 6: endorsed as neighbour sourced (agent 26 in month 5)
Month 7: replaced by action 8472 (appeared in month 5 as neighbour
sourced, now endorsed 4 times, including by the most alike
neighbour – agent 50)
52. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 52
Policy Agent - Behaviour
• After the first month of dry conditions, suggests
AFV actions to all households
• These actions are then included in the list of those
considered by the households
• If the household’s weights predispose it, it may
decide to adopt these actions
• Some other neighbours might imitate these
actions etc.
53. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 53
Number of consecutive dry months in
historical scenario
0
1
2
3
4
5
6
7
8
9
J-73
J-74
J-75
J-76
J-77
J-78
J-79
J-80
J-81
J-82
J-83
J-84
J-85
J-86
J-87
J-88
J-89
J-90
J-91
J-92
J-93
J-94
J-95
J-96
J-97
Simulation Date
Numberofconsequativedrymonths
54. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 54
Simulated Monthly Water
Consumption
REL_CHNG
.075
.063
.050
.037
.025
.012
-.000
-.013
-.025
-.038
-.050
120
100
80
60
40
20
0
Std. Dev = .01
Mean= -.000
N = 325.00
55. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 55
Monthly Water Consumption (again)
REL_CHNG
.88
.75
.63
.50
.38
.25
.13
0.00
-.13
-.25
-.38
-.50
20
10
0
Std.Dev = .17
Mean = .01
N = 81.00
56. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 56
Simulated Change in Monthly
Consumption
Date
SEP
1997
APR
1996
N
O
V
1994
JU
N
1993
JAN
1992
AU
G
1990
M
AR
1989
O
C
T
1987
M
AY
1986
D
EC
1984
JU
L
1983
FE
B
1982
SEP
1980
APR
1979
N
O
V
1977
JU
N
1976
JAN
1975
AU
G
1973
M
AR
1972
O
C
T
1970
REL_CHNG
.10
.08
.06
.04
.02
0.00
-.02
-.04
-.06
57. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 57
Relative Change in Monthly
Consumption (again)
Date
FEB
2001
SEP
2000
APR
2000
N
O
V
1999
JU
N
1999
JAN
1999
AU
G
1998
M
AR
1998
O
C
T
1997
M
AY
1997
D
EC
1996
JU
L
1996
FEB
1996
SEP
1995
APR
1995
N
O
V
1994
JU
N
1994
REL_CHNG
1.0
.8
.6
.4
.2
-.0
-.2
-.4
-.6
58. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 58
30% Neigh. biased, historical
scenario, historical innov. datesAggregate demand series scaled so 1973=100
0
20
40
60
80
100
120
140
160
180
200
J-
73
J-
74
J-
75
J-
76
J-
77
J-
78
J-
79
J-
80
J-
81
J-
82
J-
83
J-
84
J-
85
J-
86
J-
87
J-
88
J-
89
J-
90
J-
91
J-
92
J-
93
J-
94
J-
95
J-
96
J-
97
Simulation Date
RelativeDemand
59. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 59
80% Neigh. biased, historical
scenario, historical innov. datesAggregate demand series scaled so 1973=100
0
20
40
60
80
100
120
140
160
180
200
J-
73
J-
74
J-
75
J-
76
J-
77
J-
78
J-
79
J-
80
J-
81
J-
82
J-
83
J-
84
J-
85
J-
86
J-
87
J-
88
J-
89
J-
90
J-
91
J-
92
J-
93
J-
94
J-
95
J-
96
J-
97
Simulation Date
RelativeDemand
60. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 60
80% Neigh. biased, medium-high
scenario, historical innov. DatesAggregate demand series scaled so 1973=100
0
20
40
60
80
100
120
140
160
180
200
Jan-
73
Jan-
74
Jan-
75
Jan-
76
Jan-
77
Jan-
78
Jan-
79
Jan-
80
Jan-
81
Jan-
82
Jan-
83
Jan-
84
Jan-
85
Jan-
86
Jan-
87
Jan-
88
Jan-
89
Jan-
90
Jan-
91
Jan-
92
Jan-
93
Jan-
94
Jan-
95
Jan-
96
Jan-
97
Simulation Date
RelativeDemand
61. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 61
What did the model tell us?
• That it is possible that social processes:
– can cause a high and unpredictable variety in patterns
of demand
– can ‘lock-in’ behavioural patterns and partially ‘insulate’
them from outside influence (droughts only occasionally
had a permenant affect on patterns of consumption)
• and that the availability of new products could
dominate effects from changing consumptions
habits
62. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 62
Conclusions of Example 2
• ABM can be used to construct fairly-rich
computational descriptions of socially-related
phenomena which can be used
– to replicate systems analytic techniques can’t deal with
– to explore some of the possibilities
• especially those unpredictable but non-random possibilities
caused to human behaviour
– as part of an iterative cycle of detailed criticism
• validatable by both data and expert opinion
– to inform be informed by good observation
63. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 63
A central dilemma – what to trust?
Intuitions
A complex simulation
A policy maker
64. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 64
But Modeller to Policy Actor Interface
is not easy
• Analysts/modellers and policy actors have
different: goals, language, methods, habits…
• Policy Actors will often want predictions –
certainty – even if the analysts know this is
infeasible
• Analysts will know how difficult the situation is to
understand and how much is unknown, and will
want to communicate their caveats (which often
get lost in the policy process)
• So discussion between them does not necessarily
go easily
65. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 65
Many views of a model (I)
- due to syntactic complexity
• Computational ‘distance’ between specification
and outcomes means that
• There are (at least) two very different views of a
simulation
(consequences of complexity)
Simulation
Representation of OutcomesSpecification
66. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 66
Representation of Outcomes (II)
Many views of a model (II)
- understanding the simulation
(consequences of complexity)
Simulation
Representation of Outcomes (I)Specification
Analogy 1
Analogy 2
Theory 1
Theory 2
Summary 1 Summary 2
67. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 67
Four Meanings (of the PM)
Research World
1. The researcher’s idea/intention for the PM
2. The fit of the PM with the evidence/data
The ideavalidation relation extensively
discussed within research world
Policy World
3. The usefulness of the PM for decisions
4. The communicable story of the PM
The goalinterpretation relation extensively
discussed within policy world
68. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 68
Two Worlds
Research
• Ultimate Goal is
Agreement with
Observed (Truth)
• Modeller also has an
idea of what the model
is and how it works
Policy
• Ultimate Goal is in
Final Outcomes
(Usefulness)
• Decisions justified by
a communicable
causal story
Policy Model
• Labels/Documentation may be
different from all of the above!
Modeller
Policy
Advisor
69. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 69
Joining the Two Worlds
Empirical
• Ultimate Goal is
Agreement with
Observed (Truth)
• Modeller also has an
idea of what the model
is and how it works
Instrumental
• Ultimate Goal is in
Final Outcomes
(Usefulness)
• Decisions justified by
a communicable
causal story
Model
• Labels/Documentation may be
different from all of the above!
Tighter loop
(e.g. via
participatory
modelling)
70. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 70
Conclusions
• Complex systems can not be relied upon to behave in
regular ways
• Often averages, equilibria etc. are not very
informative
• Future levels can not meaningfully be predicted
• Simpler models may well make unreliable
assumptions and not be representative
• Rather complex models can be part of a risk-analysis
• Identifying some of the ways in which things can go
wrong, implement measure to watch these, then be
able to react quickly to these (‘driving policy’)
• A tight measure-react loop can be essential for driving
policy – modelling might help in this – but this is hard!
71. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 71
The End!
Bruce Edmonds:
http://bruce.edmonds.name
These Slides: http://slideshare.net/bruceedmonds
Centre for Policy Modelling: http://cfpm.org
72. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 72
Some Pitfalls in Model Construction
Pitfalls Part 1
73. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 73
Modelling Assumptions
• All models are built on assumptions, but…
• They have different origins and reliability, e.g.:
– Empirical evidence
– Other well-defined theory
– Expert Opinion
– Common-sense
– Tradition
– Stuff we had to assume to make the model possible
• Choosing assumptions is part of the art of
simulation but which assumptions are used
should be transparent and one should be honest
about their reliability – plausibility is not enough!
74. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 74
Theoretical Spectacles
• Our conceptions and models constrain how we
1. look for evidence (e.g. where and what kinds)
2. what kind of models we develop
3. how we evaluate any results
• This is Kuhn’s “Theoretical Spectacles” (1962)
– e.g. continental drift
• This is MUCH stronger for a complex simulation
we have immersed ourselves in
• Try to remember that just because it is useful to
think of the world through our model, this does
not make them valid or reliable
75. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 75
Over-Simplified Models
• Although simple models have many pragmatic
advantages (easier to check, understand etc.)…
• If we have missed out key elements of what is being
modelled it might be completely wrong!
• Playing with simple models to inform formal and
intuitive understanding is an OK scientific practice
• …but it can be dangerous when informing policy
• Simple does not mean it is roughly correct, or more
general or gives us useful intuitions
• Need to accept that many modelling tasks requested
of us by policy makers are not wise to do with
restricted amounts of time/data/resources
76. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 76
Underestimating model limitations
• All models have limitations
• They are only good for certain things: a model
that explains well might not predict well
• The may well fail when applied in a different
context than the one they were developed in
• Policy actors often do not want to know about
limitations and caveats
• Not only do we have to be 100% honest about
these limitations, but we also have to ensure that
these limitations are communicated with the
model
77. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 77
Not checking & testing a model
thoroughly
• Doh!
• Sometimes there is not a clear demarcation
between an exploratory phase of model
development and its application to serious
questions (whose answers will impact on others)
• Sometimes an answer is demanded before
thorough testing and checking can be done – “Its
OK, I just want an approximate answer” :-/
• Sometimes researchers are not honest
• Depends on the potential harm if the model is
relied on (at all) and turns out to be wrong
78. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 78
Some Pitfalls in Model Application
Pitfalls Part 2
79. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 79
Insufficiently Validated Models
• One can not rely on a model until it has been
rigorously checked and tested against reality
• Plausibility is nowhere NEAR enough
• This needs to be on more than one case
• Its better if this is done independently
• You can not validate a model using one set of
settings/cases then rely on it in another
• Validation usually takes a long time
• Iterated development and validation over many
cycles is better than one-off models (for policy)
80. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 80
Promising too much
• Modellers are in a position to see the potential of
their work, and so can tantalise others by
suggesting possible/future uses (e.g. in the
conclusions of papers or grant applications)
• They are tempted to suggest they can ‘predict’,
‘evaluate the impact of alternative polices’ etc.
• Especially with complex situations (that ABM is
useful for) this is simply deceptive
• ‘Giving a prediction to a policy maker is like giving
a sharp knife to a child’
81. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 81
The inherent plausibility of ABMs
• Due to the way ABMs map onto reality in a
common-sense manner (e.g. peopleagents)…
• …visualisations of what is happening can be
readily interpretted by non-modellers
• and hence given much greater credence than
they warrant (i.e. the extent of their validation)
• It is thus relatively easy to persuade using a good
ABM and visualisation
• Only we know how fragile they are, and need to
be especially careful about suggesting otherwise
82. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 82
Model Spread
• On of the big advantages of formal models is that
they can be passed around to be checked, played
with, extended, used etc.
• However once a model is out there, it might get
used for different purposes than intended
• e.g. the Black-Scholes model of derivative pricing
• Try to ensure a released model is packaged with
documentation that warns of its uses and
limitations
83. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 83
Narrowing the evidential base
• The case of the Newfoundland cod, indicates how
models can work to constrain the evidence base,
therefore limiting decision making
• If a model is considered authoritative, then the
data it uses and produces can sideline other
sources of evidence
• Using a model rather than measuring lots of stuff
is cheap, but with obvious dangers
• Try to ensure models are used to widen the
possibilities considered, rather than limit them
84. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 84
Other/General Pitfalls
Pitfalls Part 3
85. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 85
Confusion over model purpose
• A model is not a picture of reality, but a tool
• A tool has a particular purpose
• A tool good for one purpose is probably not good
for another
• These include: prediction, explanation, as an
analogy, an illustration, a description, for theory
exploration, or for mediating between people
• Modellers should be 100% clear under which
purpose their model is to be judged
• Models need to be justified for each purpose
separately
86. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 86
When models are used out of the
context they were designed for
• Context matters!
• In each context there will be many
conditions/assumptions we are not even aware of
• A model designed in one context may fail for
subtle reasons in another (e.g. different ontology)
• Models generally need re-testing, re-validating
and often re-developing in new contexts
87. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 87
What models cannot reasonably do
• Many questions are beyond the realm of models
and modellers but are essentially
– ethical
– political
– social
– semantic
– symbolic
• Applying models to these (outside the walls of
our academic asylum) can confuse and distract
88. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 88
The uncertainty is too great
• Required reliability of outcome values is too low
for purpose
• Can be due to data or model reasons
• Radical uncertainty is when its not a question of
degree but the situation might fundamentally
change or be different from the model
• Error estimation is only valid in absence of radical
uncertainly (which is not the case in almost all
ecological, technical or social simulations)
• Just got to be honest about this and not only
present ‘best case’ results
89. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 89
A false sense of security
• If the outcomes of a model give a false sense of
certainly about outcomes then a model can be
worse than useless; positively damaging to policy
• Better to err on the side of caution and say there
is not good model in this case
• Even if you are optimistic for a particular model
• Distinction here between probabilistic and
possibilistic views
90. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 90
Not more facts, but values!
• Sometimes it is not facts and projections that are
the issue but values
• However good models are, the ‘engineering’
approach to policy (enumerate policies, predict
impact of each, choose best policy) might be
inappropriate
• Modellers caught on the wrong side of history
may be blamed even though they were just doing
the technical parts