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.
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
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.
Session 2 into to qualitative research introAngela Ferrara
This document provides guidance on conducting qualitative research. It discusses key aspects of the research process such as developing a conceptual framework, determining what and who to study, collecting data through methods like interviews and observation, and analyzing the data through techniques such as coding and creating displays. The document emphasizes generating conclusions that consider alternative explanations and testing findings for reliability and generalizability.
1. The document presents a computational model of creative ideation that explores the value of "bad ideas" in generating new ideas.
2. The model shows that the highest number of total ideas results from an exploration/exploitation rate of .25/.75, due to higher idea variances enabling more exploitation.
3. The model distinguishes between different types of idea connections, finding that "low-accessibility ideas can lead to uncommon ideas" and that "easy shortcuts to rare ideas" exist, demonstrating the potential value of seemingly bad ideas.
This document summarizes a lecture on research methods in sociology. It discusses experiments using the ultimatum game, collecting social network data from students, and using agent-based modeling to simulate phenomena like residential segregation. It encourages thinking about how to address the limitations of surveys and interviews when studying topics like rumors. The document provides instructions for an in-class experiment and encourages students to ask questions about their research proposal assignments or feedback.
Co-creating dimensions and examples using design space gapsStephen MacNeil
This paper explores ways in which a computational co-creative agent might help people to create dimensional representations. This work was based on the difficulty people have shown in creating dimensional representations. For more information, check out the paper (https://www.researchgate.net/publication/330763999_Co-Creating_Dimensions_and_Examples_Using_Design_Space_Gaps)
The document discusses a student's reflections on a school project about cell phones. The student wondered if rumors and gossip spread through cell phones would prevent their use in school. The project showed how groups can work together to accomplish goals and connect different subject areas like science and math. The student plans to be a better team member and leader in future projects by taking charge and listening to other ideas.
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
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.
Session 2 into to qualitative research introAngela Ferrara
This document provides guidance on conducting qualitative research. It discusses key aspects of the research process such as developing a conceptual framework, determining what and who to study, collecting data through methods like interviews and observation, and analyzing the data through techniques such as coding and creating displays. The document emphasizes generating conclusions that consider alternative explanations and testing findings for reliability and generalizability.
1. The document presents a computational model of creative ideation that explores the value of "bad ideas" in generating new ideas.
2. The model shows that the highest number of total ideas results from an exploration/exploitation rate of .25/.75, due to higher idea variances enabling more exploitation.
3. The model distinguishes between different types of idea connections, finding that "low-accessibility ideas can lead to uncommon ideas" and that "easy shortcuts to rare ideas" exist, demonstrating the potential value of seemingly bad ideas.
This document summarizes a lecture on research methods in sociology. It discusses experiments using the ultimatum game, collecting social network data from students, and using agent-based modeling to simulate phenomena like residential segregation. It encourages thinking about how to address the limitations of surveys and interviews when studying topics like rumors. The document provides instructions for an in-class experiment and encourages students to ask questions about their research proposal assignments or feedback.
Co-creating dimensions and examples using design space gapsStephen MacNeil
This paper explores ways in which a computational co-creative agent might help people to create dimensional representations. This work was based on the difficulty people have shown in creating dimensional representations. For more information, check out the paper (https://www.researchgate.net/publication/330763999_Co-Creating_Dimensions_and_Examples_Using_Design_Space_Gaps)
The document discusses a student's reflections on a school project about cell phones. The student wondered if rumors and gossip spread through cell phones would prevent their use in school. The project showed how groups can work together to accomplish goals and connect different subject areas like science and math. The student plans to be a better team member and leader in future projects by taking charge and listening to other ideas.
Analysing a Complex Agent-Based Model Using Data-Mining TechniquesBruce Edmonds
A talk given at "Social Simulation 2014" at Barcelona in September.
A complex “Data Integration Model” of voter behaviour is described. However it is very complex and hard to analyse. For such a model “thin” samples of the outcomes using classic parameter sweeps are inadequate. In order to get a more holistic picture of its behaviour data- mining techniques are applied to the data generated by many runs of the model, each with randomised parameter values.
Paper is at: http://cfpm.org/aacabm/analysing a complex model-v3.4.pdf
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
This document summarizes Bruce Edmonds' presentation on different purposes of modeling. It discusses how models can be used for prediction, explanation, theoretical exploration, and analogy, but each model must be justified for its specific purpose. It argues against vague notions of "theory" and advocates for developing more general models through a staged process of building on empirical models in gentler steps of abstraction, rather than through large leaps. The goal is to justify models and their relationships through empirical work before claiming general theories.
Design thinking for science communicationphysicsdavid
The document outlines David Harris's presentation on using design thinking for science communication, where he defines key terms like design, graphic design, and design thinking; discusses models for solving problems in science communication like the information deficit model and design thinking; and provides examples of using design thinking concepts to improve explaining theoretical physics concepts to lay audiences like the Higgs mechanism and special relativity.
The document discusses various topics related to research methodology, including definitions of research, objectives of social science research, characteristics of research, the research process, types of research, and establishing hypotheses. It provides definitions of research from various scholars and outlines the key steps in conducting research, from establishing the need, to defining the problem, reviewing literature, establishing objectives and hypotheses, determining research design and methodology, collecting and analyzing data, and writing the research report. It also distinguishes between different types of research such as pure vs applied research, exploratory vs descriptive research, and experimental vs analytical research.
Mixedmethods basics: Systematic, integrated mixed methods and textbooks, NVIVOWendy Olsen
I define mixed methods and show that systematic mixed methods can be well organised, with transparent data coding and case-wise data held carefully for hypothesis testing. I list the relevant textbooks. I challenge the schism idea that qualitative methods are intrinsically opposed to what is usually done with quantitative methods. I show how an integrated approach can be begun, giving examples. Suitable to professional researchers, those doing focus groups, and those wanting more background for their qualitative research to come from quantitative data.
Berlin Summer School Presentation Olsen Data Epistemology and Methods Paradig...Wendy Olsen
Berlin Summer School in Social Science. Presentation by Wendy Olsen on Epistemology (Aspects of Knowing) in Methodological Paradigms (Schools of Thought)
Realism, Constructivism, Positivism, Empiricism
Data, Epistemology, Methodology, and Methods Paradigms. Data Collection [book] London: Sage 2012 Date of presentation, July 23, 2014.
This presentation discusses modelling and knowledge. It explores how formal models are used as representations to help understand the world. Models can be used for description, prediction, explanation, illustration and exploration. The talk examines different types of formal models, including analytical, statistical and computational models. It also discusses some common issues with formal models, such as overgeneralization and treating contextual variation as randomness. The presentation considers how to properly evaluate the strengths and weaknesses of different modelling approaches.
Scientific writing is important for publishing research, writing reports, and grant proposals. It is a skill that requires practice. Scientific writing should be well-organized, use an impersonal style with the passive voice, and keep language clear and concise using simple terminology. Proper grammar and punctuation are essential for clarity, including accurate use of commas, quotation marks, and other punctuation. The goal is clear, concise, correct and complete communication of scientific information and findings.
This document provides an overview and teaching notes for a book titled "Scientific Research in Information Systems: A Beginner's Guide" by Professor Jan Recker. The book covers basic principles of research, conducting research, and publishing research. It discusses topics like general principles of science, constructing research questions, research design including induction, deduction and abduction, and planning a research project. The teaching notes provide an outline of the book's content and chapters, and cover key concepts from the book in more detail like research design considerations and choosing a research methodology.
The Post-Truth Drift in Social SimulationBruce Edmonds
Bruce Edmonds discusses the potential for "post-truth drift" in social simulation, where models are presented in a way that prioritizes impact over truth. He identifies several ways this can occur: 1) not clearly stating a model's purpose; 2) overly relying on assumptions of simplicity without evidence; 3) over reliance on existing theories without empirical validation; 4) using analogical reasoning to claim false generality; and 5) using ambiguous language. Edmonds advocates clearly communicating a model's limitations and intended purpose to avoid misrepresentation.
1. McNemar Test- Determine whether participants with low self este.docxjackiewalcutt
1. McNemar Test- Determine whether participants with low self esteem before a series of counseling sessions decreased after counseling.
2. Fishers exact test- in a study including 20 patients, 9 women and 11 men, the success of a treatment is recorded (1 = successful, 0 =no success). Is there a difference between the success rate in men and women?
3. Chi-Square one-sample test- this test could be used to determine if a bag of marbles contains equal portions of colors. Ex: blue, red, yellow, green (equal number of each)
4. Kolmogorov-Smirnov Test-
5. The sign test or median test- 15 patients with memory loss are tested on the percentage of memory loss. Is therapy an effective method compared with the expected median memory loss over the same period of time of 20%?
6. Mann-Whitney Test-Is an off brand laundry detergent as effective as a name brnad detergent?
NATS1795 Term Project: News Brief Form
ARTICLE INFORMATION (include title, publication date and URL)
A New Fleet Of Robot Asteroid Prospectors Will Launch By 2015, 1/22/2013, http://www.popsci.com/science/article/2013-01/new-fleet-cubesat-asteroid-prospectors-will-fly-near-earth-space-rocks-2015
NEWS BRIEF RECIPIENT (include name, title and organization)
Charles F. Bolden Jr., Administrator, National Aeronautics and Space Administration (NASA)
NEWS SUMMARY (250 words minimum)
It was recently reported that a new company called Deep Space Industries (DSI) is planning a series of missions to mine asteroids as early as 2015. DSI is the 2nd company to unveil such plans, the first being competitor Planetary Resources (PRI), which formed in the spring of 2012 and receives funding from such high-profile personalities as filmmaker James Cameron, the founders and CEO of Google, and the son of former presidential candidate Ross Perot.
DSI’s ultimate goal is to mine asteroids for materials which can fuel their “MicroGravity Foundry”, which is essentially a 3D printer in space. 3D printers are capable of producing three dimensional metal objects by laying down successive layers of material and are already in use in a number of industries.1 DSI claims that by placing this technology in the proximity of asteroids, it could serve as a factory for manufacturing parts for communication satellites, space stations and future space missions. The company also states that asteroid mining could provide a source of fuel for satellites.
DSI intends to achieve its objective by beginning with a series of surveillance missions planned for 2015-2020. These will begin with two sets of small satellites, which will study the chemical compositions of Near-Earth Asteroids (ie, asteroids with orbits that pass within ~195 million km of the Sun and may therefore be capable of intersecting Earth’s path2 ). The next set of missions includes a fleet of 70-pound unmanned space crafts (called “Dragonflies”), which will fly to selected asteroids and extract 60 to 150 pounds of space rock, then return the samples to Earth ...
Open Innovation: The important of tapping into external expertise Ideon Open
At Hands On Open Innovation workshop, JOIN Business & Technology AB, shared their view of managing open innovation and creative process. The presentation focuses on open innovation and closed innovation approaches based on a case story and draws conclusions from them. It than moves to the topic of creative process and wraps up by focusing on importance of "learning by doing".
More info about the event at http://www.ideonopen.com/events
This document outlines the learning objectives and content for a course on research methods for computer science and software engineering. The objectives include explaining the purpose of research, understanding basic research concepts, acquiring skills to formulate research problems and design research projects. The document discusses different research approaches like quantitative, qualitative and design science methods. It also covers topics like theories, constructs, variables, conceptual frameworks, propositions, and hypotheses. The teaching methods will include lectures, group work, projects and presentations. Students will be evaluated based on assignments, exams and participation.
This document discusses language development and structure. It begins by outlining the learning objectives, which are to describe language structure and its flaws, identify stages of language development, and distinguish between Chomsky and Skinner's views of language development. It then defines key parts of language structure, such as phonemes, morphemes, grammar, and discusses Chomsky's views on surface structure and deep structure. The document also outlines flaws in language semantics, syntax, and developmental stages of language learning in children. It concludes by contrasting Chomsky and Skinner's theories of language development.
Paper to prototype, or.... How I learned to stop worrying and love ScienceChris McQueen
Post it to prototype, or.... How I learned to stop worrying and love Science.
Findings from a personal struggle with my "designer" identity.
Part 1: Why think like a scientist? Includes a short story…
Part 2: A science/design project.
The document discusses problem solving and creativity. It outlines Edward Torrance's four criteria for creativity: fluency, flexibility, elaboration, and originality. It then provides examples and activities to practice each criterion. The document also discusses Torrance's framework for creative thinking and outlines the six stages of creative problem solving: mess finding, data finding, problem finding, idea finding, solution finding, and acceptance finding. Key aspects of each stage are briefly described.
Trendspotting – Models of Man (In Design Thinking)Tan Ti
The document discusses trends in models of human behavior used in design thinking methods over time. It argues that design thinking methods have evolved in parallel with philosophical assumptions about human rationality, moving from intuitive designer to bounded rationality to reflective practitioner. However, it critiques this view for lacking consideration of opposing perspectives and proposes three reasons why the current reflective practice paradigm may be outdated: 1) the evidence linking models of man to method evolution has flaws, 2) current research suggests humans are more irrational than bounded rationality assumes, and 3) the underlying problem-solution method is falling out of favor despite its reflective aspects. It hypothesizes that understanding the drivers of method evolution could help predict and influence future trends.
Role of theory in management research -- Dr Yasser BhattiYasser Bhatti
The document discusses the role of theory in management research. It begins by defining key concepts like theory, methodology, and method. It then discusses how theories can help make sense of the world, understand causation, predict outcomes, and guide research. Theories are developed and tested through empirical research, with adjustments made based on findings. Good theories are parsimonious, broad, accurate, and falsifiable. The document emphasizes that theories are approximations rather than proven truths.
Computing the Sociology of Survival – how to use simulations to understand c...Bruce Edmonds
This document summarizes Bruce Edmonds' presentation on using simulations to understand complex socio-ecological systems and address ecological survival issues. The presentation discusses using an integrated socio-ecological modeling approach, where an individual-based ecological model is combined with agent-based human societies to analyze their interactions over long time periods. The model simulates evolving food webs and the impacts of introducing human agents who can learn and innovate. Preliminary results show humans rapidly impacting ecosystem populations and diversity by becoming top predators and filling multiple niches. The approach aims to better understand how societal structures influence environmental decision-making.
This document discusses various aspects of marketing research including defining the research problem, reviewing literature, determining sample design, collecting data through various methods like surveys and questionnaires, and analyzing the data. It provides details on formulating the research problem, developing the research design, using probability and non-probability sampling techniques, and methods for collecting primary data such as observation, interviews, and questionnaires. The key stages of marketing research are also summarized: problem definition, research design, field work, data analysis, report presentation, and decision making.
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
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
More Related Content
Similar to Modelling Innovation – some options from probabilistic to radical
Analysing a Complex Agent-Based Model Using Data-Mining TechniquesBruce Edmonds
A talk given at "Social Simulation 2014" at Barcelona in September.
A complex “Data Integration Model” of voter behaviour is described. However it is very complex and hard to analyse. For such a model “thin” samples of the outcomes using classic parameter sweeps are inadequate. In order to get a more holistic picture of its behaviour data- mining techniques are applied to the data generated by many runs of the model, each with randomised parameter values.
Paper is at: http://cfpm.org/aacabm/analysing a complex model-v3.4.pdf
Different Modelling Purposes - an 'anit-theoretical' approachBruce Edmonds
This document summarizes Bruce Edmonds' presentation on different purposes of modeling. It discusses how models can be used for prediction, explanation, theoretical exploration, and analogy, but each model must be justified for its specific purpose. It argues against vague notions of "theory" and advocates for developing more general models through a staged process of building on empirical models in gentler steps of abstraction, rather than through large leaps. The goal is to justify models and their relationships through empirical work before claiming general theories.
Design thinking for science communicationphysicsdavid
The document outlines David Harris's presentation on using design thinking for science communication, where he defines key terms like design, graphic design, and design thinking; discusses models for solving problems in science communication like the information deficit model and design thinking; and provides examples of using design thinking concepts to improve explaining theoretical physics concepts to lay audiences like the Higgs mechanism and special relativity.
The document discusses various topics related to research methodology, including definitions of research, objectives of social science research, characteristics of research, the research process, types of research, and establishing hypotheses. It provides definitions of research from various scholars and outlines the key steps in conducting research, from establishing the need, to defining the problem, reviewing literature, establishing objectives and hypotheses, determining research design and methodology, collecting and analyzing data, and writing the research report. It also distinguishes between different types of research such as pure vs applied research, exploratory vs descriptive research, and experimental vs analytical research.
Mixedmethods basics: Systematic, integrated mixed methods and textbooks, NVIVOWendy Olsen
I define mixed methods and show that systematic mixed methods can be well organised, with transparent data coding and case-wise data held carefully for hypothesis testing. I list the relevant textbooks. I challenge the schism idea that qualitative methods are intrinsically opposed to what is usually done with quantitative methods. I show how an integrated approach can be begun, giving examples. Suitable to professional researchers, those doing focus groups, and those wanting more background for their qualitative research to come from quantitative data.
Berlin Summer School Presentation Olsen Data Epistemology and Methods Paradig...Wendy Olsen
Berlin Summer School in Social Science. Presentation by Wendy Olsen on Epistemology (Aspects of Knowing) in Methodological Paradigms (Schools of Thought)
Realism, Constructivism, Positivism, Empiricism
Data, Epistemology, Methodology, and Methods Paradigms. Data Collection [book] London: Sage 2012 Date of presentation, July 23, 2014.
This presentation discusses modelling and knowledge. It explores how formal models are used as representations to help understand the world. Models can be used for description, prediction, explanation, illustration and exploration. The talk examines different types of formal models, including analytical, statistical and computational models. It also discusses some common issues with formal models, such as overgeneralization and treating contextual variation as randomness. The presentation considers how to properly evaluate the strengths and weaknesses of different modelling approaches.
Scientific writing is important for publishing research, writing reports, and grant proposals. It is a skill that requires practice. Scientific writing should be well-organized, use an impersonal style with the passive voice, and keep language clear and concise using simple terminology. Proper grammar and punctuation are essential for clarity, including accurate use of commas, quotation marks, and other punctuation. The goal is clear, concise, correct and complete communication of scientific information and findings.
This document provides an overview and teaching notes for a book titled "Scientific Research in Information Systems: A Beginner's Guide" by Professor Jan Recker. The book covers basic principles of research, conducting research, and publishing research. It discusses topics like general principles of science, constructing research questions, research design including induction, deduction and abduction, and planning a research project. The teaching notes provide an outline of the book's content and chapters, and cover key concepts from the book in more detail like research design considerations and choosing a research methodology.
The Post-Truth Drift in Social SimulationBruce Edmonds
Bruce Edmonds discusses the potential for "post-truth drift" in social simulation, where models are presented in a way that prioritizes impact over truth. He identifies several ways this can occur: 1) not clearly stating a model's purpose; 2) overly relying on assumptions of simplicity without evidence; 3) over reliance on existing theories without empirical validation; 4) using analogical reasoning to claim false generality; and 5) using ambiguous language. Edmonds advocates clearly communicating a model's limitations and intended purpose to avoid misrepresentation.
1. McNemar Test- Determine whether participants with low self este.docxjackiewalcutt
1. McNemar Test- Determine whether participants with low self esteem before a series of counseling sessions decreased after counseling.
2. Fishers exact test- in a study including 20 patients, 9 women and 11 men, the success of a treatment is recorded (1 = successful, 0 =no success). Is there a difference between the success rate in men and women?
3. Chi-Square one-sample test- this test could be used to determine if a bag of marbles contains equal portions of colors. Ex: blue, red, yellow, green (equal number of each)
4. Kolmogorov-Smirnov Test-
5. The sign test or median test- 15 patients with memory loss are tested on the percentage of memory loss. Is therapy an effective method compared with the expected median memory loss over the same period of time of 20%?
6. Mann-Whitney Test-Is an off brand laundry detergent as effective as a name brnad detergent?
NATS1795 Term Project: News Brief Form
ARTICLE INFORMATION (include title, publication date and URL)
A New Fleet Of Robot Asteroid Prospectors Will Launch By 2015, 1/22/2013, http://www.popsci.com/science/article/2013-01/new-fleet-cubesat-asteroid-prospectors-will-fly-near-earth-space-rocks-2015
NEWS BRIEF RECIPIENT (include name, title and organization)
Charles F. Bolden Jr., Administrator, National Aeronautics and Space Administration (NASA)
NEWS SUMMARY (250 words minimum)
It was recently reported that a new company called Deep Space Industries (DSI) is planning a series of missions to mine asteroids as early as 2015. DSI is the 2nd company to unveil such plans, the first being competitor Planetary Resources (PRI), which formed in the spring of 2012 and receives funding from such high-profile personalities as filmmaker James Cameron, the founders and CEO of Google, and the son of former presidential candidate Ross Perot.
DSI’s ultimate goal is to mine asteroids for materials which can fuel their “MicroGravity Foundry”, which is essentially a 3D printer in space. 3D printers are capable of producing three dimensional metal objects by laying down successive layers of material and are already in use in a number of industries.1 DSI claims that by placing this technology in the proximity of asteroids, it could serve as a factory for manufacturing parts for communication satellites, space stations and future space missions. The company also states that asteroid mining could provide a source of fuel for satellites.
DSI intends to achieve its objective by beginning with a series of surveillance missions planned for 2015-2020. These will begin with two sets of small satellites, which will study the chemical compositions of Near-Earth Asteroids (ie, asteroids with orbits that pass within ~195 million km of the Sun and may therefore be capable of intersecting Earth’s path2 ). The next set of missions includes a fleet of 70-pound unmanned space crafts (called “Dragonflies”), which will fly to selected asteroids and extract 60 to 150 pounds of space rock, then return the samples to Earth ...
Open Innovation: The important of tapping into external expertise Ideon Open
At Hands On Open Innovation workshop, JOIN Business & Technology AB, shared their view of managing open innovation and creative process. The presentation focuses on open innovation and closed innovation approaches based on a case story and draws conclusions from them. It than moves to the topic of creative process and wraps up by focusing on importance of "learning by doing".
More info about the event at http://www.ideonopen.com/events
This document outlines the learning objectives and content for a course on research methods for computer science and software engineering. The objectives include explaining the purpose of research, understanding basic research concepts, acquiring skills to formulate research problems and design research projects. The document discusses different research approaches like quantitative, qualitative and design science methods. It also covers topics like theories, constructs, variables, conceptual frameworks, propositions, and hypotheses. The teaching methods will include lectures, group work, projects and presentations. Students will be evaluated based on assignments, exams and participation.
This document discusses language development and structure. It begins by outlining the learning objectives, which are to describe language structure and its flaws, identify stages of language development, and distinguish between Chomsky and Skinner's views of language development. It then defines key parts of language structure, such as phonemes, morphemes, grammar, and discusses Chomsky's views on surface structure and deep structure. The document also outlines flaws in language semantics, syntax, and developmental stages of language learning in children. It concludes by contrasting Chomsky and Skinner's theories of language development.
Paper to prototype, or.... How I learned to stop worrying and love ScienceChris McQueen
Post it to prototype, or.... How I learned to stop worrying and love Science.
Findings from a personal struggle with my "designer" identity.
Part 1: Why think like a scientist? Includes a short story…
Part 2: A science/design project.
The document discusses problem solving and creativity. It outlines Edward Torrance's four criteria for creativity: fluency, flexibility, elaboration, and originality. It then provides examples and activities to practice each criterion. The document also discusses Torrance's framework for creative thinking and outlines the six stages of creative problem solving: mess finding, data finding, problem finding, idea finding, solution finding, and acceptance finding. Key aspects of each stage are briefly described.
Trendspotting – Models of Man (In Design Thinking)Tan Ti
The document discusses trends in models of human behavior used in design thinking methods over time. It argues that design thinking methods have evolved in parallel with philosophical assumptions about human rationality, moving from intuitive designer to bounded rationality to reflective practitioner. However, it critiques this view for lacking consideration of opposing perspectives and proposes three reasons why the current reflective practice paradigm may be outdated: 1) the evidence linking models of man to method evolution has flaws, 2) current research suggests humans are more irrational than bounded rationality assumes, and 3) the underlying problem-solution method is falling out of favor despite its reflective aspects. It hypothesizes that understanding the drivers of method evolution could help predict and influence future trends.
Role of theory in management research -- Dr Yasser BhattiYasser Bhatti
The document discusses the role of theory in management research. It begins by defining key concepts like theory, methodology, and method. It then discusses how theories can help make sense of the world, understand causation, predict outcomes, and guide research. Theories are developed and tested through empirical research, with adjustments made based on findings. Good theories are parsimonious, broad, accurate, and falsifiable. The document emphasizes that theories are approximations rather than proven truths.
Computing the Sociology of Survival – how to use simulations to understand c...Bruce Edmonds
This document summarizes Bruce Edmonds' presentation on using simulations to understand complex socio-ecological systems and address ecological survival issues. The presentation discusses using an integrated socio-ecological modeling approach, where an individual-based ecological model is combined with agent-based human societies to analyze their interactions over long time periods. The model simulates evolving food webs and the impacts of introducing human agents who can learn and innovate. Preliminary results show humans rapidly impacting ecosystem populations and diversity by becoming top predators and filling multiple niches. The approach aims to better understand how societal structures influence environmental decision-making.
This document discusses various aspects of marketing research including defining the research problem, reviewing literature, determining sample design, collecting data through various methods like surveys and questionnaires, and analyzing the data. It provides details on formulating the research problem, developing the research design, using probability and non-probability sampling techniques, and methods for collecting primary data such as observation, interviews, and questionnaires. The key stages of marketing research are also summarized: problem definition, research design, field work, data analysis, report presentation, and decision making.
Similar to Modelling Innovation – some options from probabilistic to radical (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
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
Modelling Pitfalls - introduction and some casesBruce Edmonds
This document discusses several case studies of agent-based models (ABMs) and some of the potential pitfalls of ABMs. It begins by outlining Bruce Edmonds' approach to discussing pitfalls, which is to start with the purpose of the simulation and think about how it could go wrong. Several examples of ABMs are then summarized, including Schelling's model of racial segregation, opinion dynamics models, a water distribution model of Bali, models for predicting US elections, and models of cooperation evolution. The document concludes by providing a summary of different ABM purposes and some of the particular risks associated with each purpose.
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.
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.
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.
Finding out what could go wrong before it does – Modelling Risk and UncertaintyBruce Edmonds
The document discusses challenges with using models to predict outcomes for complex systems and situations. It describes how classic policy modeling approaches often make simplifying assumptions that may not accurately capture real-world complexity. Specifically, statistical models assume fixed relationships that don't account for heterogeneity; microsimulations and CGE models only model simple systems and make strong assumptions; and system dynamics and simulation models have limitations due to unknown parameters and structural changes. The document advocates approaches that rapidly sense and react to complex, uncertain systems instead of trying to predict them, as predicting complex, unpredictable situations can provide false confidence and lead to unforeseen consequences.
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.
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.
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
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.
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
This document summarizes Bruce Edmonds' presentation on simulating superdiversity using agent-based social simulation. The presentation aims to illustrate how social simulation can be used to explore issues of diversity. It discusses how agent-based simulation works by representing individual agents with behavioral rules and simulating their interactions. While the model presented is abstract, agent-based simulation allows emergent phenomena to be studied and the link between micro and macro levels to be explored. Historical examples of related simulations on segregation, cultural change, and ethnocentrism are also briefly discussed. The model aims to allow groupings to emerge from heterogeneous agents embedded in a social environment, going beyond predefined groups.
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.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)eitps1506
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Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
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With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
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PPT on Sustainable Land Management presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Sustainable Land Management - Climate Smart Agriculture
Modelling Innovation – some options from probabilistic to radical
1. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 1
Modelling Innovation
– some options from probabilistic to radical
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
2. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 2
The problem
Whilst there are many simulations/models of
the spread or uptake of innovations…
...the process of innovation itself is
generally not modelled
3. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 3
Talk Outline
1. Kinds of innovation (derived from Boden, M. (2004)
The creative mind: myths and mechanisms, Routledge)
2. Three examples of modelling exploratory
innovation
3. Some thoughts about how to approach modelling
transformative innovation
4. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 4
Innovation 0: Probibilistic
• When an unlikely event occurs (from a known
distribution)
Examples:
– two people who went to school together happen to
meet in a departure lounge 20 years later
– A spore of fungus happens to infect a petri dish with a
bacterial culture on it
• Not really innovation in a meaningful sense
• But it may trigger an innovation (of another kind)
• However, this is how innovation is represented in
many simulations!
5. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 5
Innovation 1: Combinatorial
• When there are a number of possible
‘components’ and you find the combination of
them (that does something)
Examples:
– The right cut, size, colour and material for a t-shirt
– Choosing the options for a new kind of family car that is
both attractive yet cheap enough to sell well
• This is hard when there are a large number of
possibilities and when the number of acceptable
solutions is low
• One can systematically compare solutions and
maybe find an optimal combination
6. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 6
Innovation 2: Exploratory
• Where the process of discovery is path-dependent
and the paths branch in complicated ways
Examples:
– Finding the right genome (for some purpose) using a
sequence of mutations and sexual recombinations
– Discovering how to synthesize a chemical
• This is closer to pure research – one might not
know the outcome before one gets there
• Not possible to optimize, this is more a case of
discovering something new (or a new process)
• Might be useful in a new way, or be a new kind
7. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 7
Innovation 3: Transformational
• When something changes the way we think about
things – changes the landscape of discovery
• Examples:
– When Newton connected the movement of everyday
objects and the planets via his laws of motion
– A new understanding of a relationship with someone
when you discover something about their past
• Adds a new ‘dimension’ into the search for
solutions, or changes the paths for exploration
• Something humans are quite good at, but this can
be misleading – just because you can think of
something in a new way does not make it so
8. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 8
Personal vs. Historical Innovation
Just because something is new for an individual does
not mean it counts as an innovation for society
For it to count as a historical innovation:
1. It has not to be commonly known or adopted already
2. It is recognized as a particular kind of thing
3. And judged as an innovation by the ‘field’ of people
that judges that kind of thing
(Kahl, CH. (2012). Creativity is more than a trait – It's a
relation, Doctoral thesis, University of Hamburg).
It may be that something is not immediately recognised
as an innovation but may be so later due to the impact
of the innovation or the field changes etc.
9. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 9
Two Examples of Modelling
Exploratory Innovation
1. A model of mathematical discovery and scientific
publishing
2. A model of making things
In both:
• Knowledge is structured in complex ways
• How to “get to” the desirable structures is difficult to
predict before you do, but there are clues in the
intermediate stages (i.e. the search space is hard but
not random)
• Items have a dual use: as ends in themselves, but
also as tools to help make new items or make items
more efficiently
• There are naturally “gateway” discoveries that the
means to obtaining many other targets
10. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 10
Mathematical Discovery and Scientific
Publishing
Example 1
11. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 11
The test-bed problem
• The theories are those of classical propositional
logic with connectives: ¬, ∧, ∨, →, ↔, T, F
• formulated as a “Hilbert System” with:
– 14 axioms
– 1 rule, Modus Ponens (MP) (explained shortly)
• 110 designated “target theories” taken from
textbooks
• New theories developed by taking applying MP to
to existing theories
• Makes for a fairly tough problem - space of
theorems is more than exponential in size
12. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 12
Agent-1
Agent-2
Structure of the Simulation
The Journal
The Axioms
MP
MP
13. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 13
Action of the MP inference rule
yyxx →→→ ))((
)()(( aaaa →→→
BA → (Major Premise)
A (Minor Premise)
)( aa →
B (Inference)
14. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 14
Agents
• Agents have two (limited) stores, for knowledge
(minor) and techniques (major)
• Each iteration each agent:
1. Decides what new items of knowledge to add to its
private stores from the published set, also which to
drop (both major and minor).
2. Decides which major premise and what set of minor
premises it will try with the MP rule and add any
results to its (minor) store.
3. Decides which of its private knowledge (that is not
already public) it will submit to the journal
• Agents may “panic” if they have not discovered
anything within a certain number of iterations
and replace their knowledge (minor or major)
15. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 15
The Journal (the Journal of Artificial
Sentences and Successful Syllogisms)
• The journal is the public repository of knowledge
(accessible to all)
• Each iteration the journal:
1. Makes a short-list of submissions that meet basic
criteria (e.g. novelty, number of vars.)
2. Ranks the short-list using a weighted score (in this
case, shortening, shortness, past success of
submitter, number of variables)
3. Chooses from the ranked short-list (e.g. top N,
randomly, probabilistically etc.)
16. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 16
The Experiment
• 20 agents over 50 iterations
• Each agent stores 4 major and 27 minor premises
as its current knowledge and submit all
unpublished formulas they find
• 1 journal, selecting for (in descending order)
shortening; shortness; prestige; num vars.
• Vary the number of formula the journal publishes
each iteration from 1…10
• Results are averages over 25 independent runs
for each setting
17. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 17
Example output from a run
…
Iteration 3
agent 3 found '->' ('->' A ('->' B C)) ('->' B ('->' A C))
agent 3 found '->' ('->' A ('->' ('->' B C) D)) ('->' ('->' B C) ('->' A D))
agent 6 found '->' ('->' A ('->' B B)) ('->' A ('->' A ('->' B B)))
agent 6 found '->' ('->' A ('->' A B)) ('->' A B)
agent 6 found '->' ('->' A B) ('->' ('->' B A) ('->' A B))
agent 17 found '->' ('->' A ('->' A B)) ('->' A B)
agent 19 found '->' ('->' A B) ('->' ('->' C A) ('->' C B))
agent 19 found '->' ('->' A B) ('->' ('->' C ('->' ('->' A B) D)) ('->' C D))
Iteration 4
agent 7 found '->' ('->' A B) ('->' ('->' ('->' A B) C) C)
agent 7 found '->' A ('->' ('->' A B) B)
agent 13 found '->' ('->' A ('¬' A)) ('¬' A)
agent 15 found '->' ('->' A ('¬' A)) ('¬' A)
Iteration 5
Iteration 6
…
18. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 18
Number of formulas in public domain
0
100
200
300
400
500
600
0 10 20 30 40 50iteration
totalnumberformulafound
njp=1
njp=2
njp=3
njp=4
njp=5
njp=6
njp=7
njp=8
njp=9
njp=10
19. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 19
Number of targets found
10
10.5
11
11.5
12
0 10 20 30 40 50iteration
totalnumbertargetsfound
njp=10
njp=9
njp=8
njp=7
njp=6
njp=5
njp=4
njp=3
njp=2
njp=1
20. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 20
Total number found by agents (also num.
submitted for publication)
0
20
40
60
80
100
120
140
160
180
0 10 20 30 40 50iteration
totalnumberformulasubmitted
njp=1
njp=2
njp=3
njp=4
njp=5
njp=6
njp=7
njp=8
njp=9
njp=10
21. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 21
(Average) spread (the SD) of numbers of
formulas found by agents
0
1
2
3
4
0 10 20 30 40 50iteration
sdofnumbersagentsfound
njp=1
njp=2
njp=3
njp=4
njp=5
njp=6
njp=7
njp=8
njp=9
njp=10
22. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 22
Number found by agents
(a single run, njp=2)
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50
Iteration
Numberfoundbyagents
23. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 23
Number found by agents
(a single run, njp=10)
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50
Iteration
Numberfoundbyagents
24. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 24
Some issues that could be
investigated in this test-bed include:
• Is any norm on methods for discovering new
knowledge is counterproductive? (Feyerabend)
• What is the effect of the framework (within which
knowledge is expressed) on the structure of new
knowledge? (Kuhn)
• When and how do social processes act to
increase the reliability of knowledge collectively
produced (or otherwise)? (Merton, Popper)
• Is it helpful to have an inviolate core of
knowledge/techniques that is not open to
revision? (Lakatos)
25. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 25
A Model of Making
Example 2
26. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 26
The String MakerWorld
• Things in this model are strings, e.g. ‘ACC&BA’
• They are made form a finite number of ‘elements’ {A,
B, C…} and the two special symbols: {&, >}
• Only certain strings can be extracted from the
environment (randomly determined at the start). All
other strings have to be made from these.
• Only certain target strings can have inherent value
(randomly determined at the start). These can be
‘used’ to get that value
• Strings can be joined/split by hand at & but to get any
other kind of longer string you have to use a tool
(another string with “>” in it that can change strings)
27. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 27
Simple Example
Say an agent was in the following situation:
Available in environment: A; A>; AA; AB; B>; BA; BB;
A&A; A&B; AAA; AB>BA
Has use value to agent: AB; A&B; AAA; AAB; ABA; B&A;
BBA; BBB; A&AA
Possible sequences of actions by agent:
• Get A&B then immediately use it
• Get A and BA then join these to make A&BA
• Get A&B, split this into A and B, then join these to
make B&A and use this
• Get AB use tool AB>BA on it to make BA, use it
28. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 28
Rationale behind String MakerWorld
• Simplest world that allows the complexity of
making to be explicitly represented
• Working out how to make valuable strings is hard,
which gives value to good plans (and hence
motivation for trading/sharing plans)
• Control over which resources each agent has
access to can add heterogeneity in production
• Control over the target strings each agent can
directly use can add heterogeneity of need
• Heterogeneity of resources and needs gives
motivation for the trade/sharing of objects
29. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 29
The Model
• Agents are patches but can interact with others in any
pattern they choose/learn
• Things are explicitly tracked with their own properties
(which matter structurally)
Agents are
implemented
as patches
Object and its string
owned by an agent
Some objects are
complex, this one soft-
joined from smaller
parts
Some objects are simple, this
one composed of a single
“element”
This object is a tool, in
this case adding a soft
join into the string
(allowing it to be maybe
separated later)
The arrow indicates a sale/
transfer of an object from one
agent to another
30. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 30
Plans
• Plans (the sequence of actions needed to make
particular things) are separate from the things
• Agents sometimes do things experimentally (ATM
at random) to see what they can make
• Agents remember how they made things in terms
of plans – the actions necessary to get any
particular outcome
• Agents remember the better value plans and
preferentially execute those again
• These plans could be sent/shared/licensed
between agents
31. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 31
Some example plans learnt by an
agent
value 3.25: realise [BAA split-right [B&BAA get]]
value 1.5: sell [B get] (patch 0 0)
value 1.25: realise [BAA split-right [B&BAA split-right
[B&B&BAA join [B split-left [B&BAA get]] [B&BAA get]]]]
value 1.25: sell [B split-left [B&BAA get]] (patch 2 0)
value -1: join-random
value -1.5: B split-left [B&BAA get]
value -2: get-random
• Note that alternative plans to make the same things
might be remembered, but with different costs
• Plans can be arbitrarily complex, thought each action
has a small cost associated with it, so more complex
plans will tend to have lower values (unless they
result in a more valuable result)
• Agents prefer to re-use plans with higher value
32. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 32
The (current) main simulation loop
Continually (each tick), agent:
Considers a number of plans (including the
default random ones) with a bias towards more
valuable ones:
Until one works:
Assess next plan to see if it would work
If so, do plan!
If new, compile and remember plan
If have too many plans in memory, maybe forget
one (with a bias towards the less valuable ones)
33. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 33
Number of things made in 10 different runs
0
20
40
60
80
100
120
140
160
0
23
46
69
92
115
138
161
184
207
230
253
276
299
322
345
368
391
414
437
460
483
1
2
3
4
5
6
7
8
9
34. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 34
Number of different things made in the 10
runs
0
5
10
15
20
25
30
0
22
44
66
88
110
132
154
176
198
220
242
264
286
308
330
352
374
396
418
440
462
484
1
2
3
4
5
6
7
8
9
35. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 35
Average number of tools found in 10 runs
0
1
2
3
4
5
6
0
21
42
63
84
105
126
147
168
189
210
231
252
273
294
315
336
357
378
399
420
441
462
483
1
2
3
4
5
6
7
8
9
36. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 36
Average String length in the 10 runs
0
5
10
15
20
25
30 0
21
42
63
84
105
126
147
168
189
210
231
252
273
294
315
336
357
378
399
420
441
462
483
1
2
3
4
5
6
7
8
9
37. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 37
Average Number of things for sale in 10
runs
0
5
10
15
20
25
0
17
34
51
68
85
102
119
136
153
170
187
204
221
238
255
272
289
306
323
340
357
374
391
408
425
442
459
476
493
1
2
3
4
5
6
7
8
9
10
38. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 38
Average Maximum Plan Value in the 10
runs
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
0
16
32
48
64
80
96
112
128
144
160
176
192
208
224
240
256
272
288
304
320
336
352
368
384
400
416
432
448
464
480
496
39. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 39
Average Wealth in the 10 runs
0
200
400
600
800
1000
1200
1400 0
18
36
54
72
90
108
126
144
162
180
198
216
234
252
270
288
306
324
342
360
378
396
414
432
450
468
486
1
2
3
4
5
6
7
8
9
10
40. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 40
Standard Deviation of Wealth in the 10 runs
0
100
200
300
400
500
600
700
800
0
16
32
48
64
80
96
112
128
144
160
176
192
208
224
240
256
272
288
304
320
336
352
368
384
400
416
432
448
464
480
496
1
2
3
4
5
6
7
8
9
10
41. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 41
Issues we might explore…
…include:
• Changing the heterogeneity of needs, from everybody
has similar needs, to all different
• Explore the conditions under which more centralised
manufacturing or markets emerge
• Explore the impact of introducing new technology
(something equivalent to 3D printers)
• Looking at how the structure of communication (for
plans or selling/sharing items) effects things
• Maybe even wilder topics, e.g.
– what if all objects contain their own plans
– or come with tools to disassemble/reassemble/fix it
– How might the norms of agents impact on the outcomes
42. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 42
Towards Modelling Transformative
Innovation
• In order to represent transformational modelling
one needs to be able to change the way agents
view what they are doing
• This means they have to
a) HAVE a view of what they are doing
b) use this to make representations of their world
c) then use these for discovery (e.g. exploratory
discovery)
d) sometimes be able to change their view
• In other words a Model of Modelling itself
43. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 43
A language of
representation
model1
model2
model3
Goals to evaluate
success of
models
Actions
Perceptions
Some of what Model of Modelling would
involve
A world sufficiently
complex to make
this complex
machinery
worthwhile
A language of
representation
model1
model2
model3
Goals to evaluate
success of
models
Actions
Perceptions
44. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 44
Conclusions
• Modelling more sophisticated kinds of innovation
is possible within agent-based simulation
• At the moment these models are quite abstract,
but there is no reason why these kinds of
approaches should not be applied to modelling
innovation within firms and universities
• A better understanding of the creativity that occurs
could help us know how to encourage and/or
direct it
• Simpler models of innovation are almost certainly
insufficient to do this
45. Modelling Innovation - some options from probabilistic to radicals, Bruce Edmonds, European Academy, May 2017. slide 45
The End!
Bruce Edmonds:
http://bruce.edmonds.name
Centre for Policy Modelling: http://cfpm.org
The slides will be available at:
http://slideshare.net/BruceEdmonds