Towards Institutional System Farming

Towards Institutional System Farming, Bruce Edmonds, Lorentz Centre, Leiden, 7th September 2016. slide 1
Towards Institutional System
Farming
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

Acknowledgements
Thanks to Emma Norling and Joanna Bryson with
whom I have specifically developed and discussed
some of these ideas but also the many people I
have argued with about all this,
including Frank Dignum!

The Complexity of Rules
Part 1:

1. Rules are context-dependent
• Even though institution rules/norms might be
relatively simple, they are usually context-
dependent…
• ...that is, when they apply is something subtle and
unconscious that is learnt during acculturation
• These contexts will not be easily formalisable...
• ...but could refer to the identity of the people
involved (e.g. lawyers or police)...
• ...or the kind of place (e.g. a library)...
• ...or even the kind of situation (e.g. a storm
coming, a birthday celebration)

2. The meaning of rules is culture-
dependent
• How a rule is applied will depend upon a large
collection of beliefs, expectations, terms, identities
• …that will come from the culture of the relevant
participants.
• In particular, the exceptions and cases where they
are particularly strong
• And when they are conventions, when norms and
when rules
• E.g “Don’t walk on the grass” does not apply to a
policeman chasing a criminal or the person
tending the grass

3. Rules rely upon knowledge of
what is possible
• Rules presume knowledge of what actions/options
are relevant in any particular situation
• One is not usually obliged to do the impossible
• But IS usually assumed to know the relevant
alternatives
• This (1) presumes a lot of background knowledge
but (2) assumes the actor can work out the
implications of this (although sometimes this is
unrealistic in terms of mental inference)
• In particular, this includes knowledge of relevant
power relationships

4. Rules are embedded within
complex sets of other rules
• Rules are rarely separable from other rules, which
may:
– Specify exceptions
– Be Meta-rules
– Overlap with other rules
– Contradict other rules
– Provide interpretation rules for rules (e.g. counts-as)
– Say which other rules apply
• Which of the related rules need to be born in mind
may depend on the context and what is possible
in the particular situation

5. Rules have to continually be
maintained and change
• As vividly illustrate by Tine’s Talk 
• Rules have to change all the time to keep up with
the reality it deals with
– New kinds of risks/threats/opportunities arise so rules
need to be re-interpreted
– New kinds of situation (context) are encountered so
they need to be adjusted/reinterpreted to deal with
them
– Interests of participants change
– The make-up of the participants may change
– Outside interests and constraints may pressure change

Design vs. Emergence
Part 2:

Engineering
• Any systematic activity that ensures that a
constructed artifact works well
• Historically, engineering fields made huge
progress in moving from a trial & error process to
one of design and careful construction to plan
• Computer scientists are taught that good pre-
construction design can reduce time spent bug-
fixing and result in more reliable code
• Essentially these techniques use: theories from
science, well tried design principles and patterns,
and techniques to aid formal representation

Pre- and Post ‘Construction’ Stages
Specification
and/or Goals
for System
test &
compareConstruction
Plan
the construction
process
Engineering/d
esign
Adaption Plan
change system
Adaption/evolution/m
aintenance

Complex Systems
• Complexity means that one can not predict how a
system will be based on inference from its design
• Or even whether a constructed system meets any
stated set of specifications
• This can be proved for many formal systems (and
all sufficiently expressive ones such as those that
include arithmetic and even quite simple
ABMs)(e.g. Edmonds & Bryson 2004)
• But is obviously true in any practical sense for
many other systems (e.g. social or biological)

Implications of such complexity
• Established techniques of specify&implement
engineering do not work…
• ...except for very simple cases or sub-
components
• One has to (at least) mix in elements of trying
things out and learning from them
• But even then, one does not know if subtle
differences in set-up will result in different
outcomes
• Thus complex systems, including institutions,
require a shift away from design towards post-
construction processes of adaption

System Farming
Part 3:

In Farming
• One deals with many complex systems
• There are very few universal principles
• But some rules of thumb…
• and a lot of specific examples and knowledge
• Design is relatively unimportant compared to
continual maintenance
• There are different rules of thumb and possible
diagnosis for each kind of animal/crop
• less optimising than simply averting crisies – stuff
going wrong
• This is quite a time-consuming and relatively
mundane task

System Farming
• When dealing with complex systems there needs
to be a shift of effort from pre-construction stages
to post-construction stages
• That is specification and design becomes less
important and monitoring and adaption becomes
more important in many different respects
• This feels wrong! We are trained as computer
scientists and careful academics that unexpected
outcomes are a signal of not enough preparation
and also maintenance has a lower status than
design so this involves a drop in prestige

Shifts (I)
• Reliability from experience rather than
careful control of construction
• Continual tinkering rather than one-off effort
• Monitoring rather than prediction
• Multiple fallible mechanisms rather than one
reliable mechanism
• Disaster aversion rather than optimising
performance

Shifts (II)
• Partial rather than full understanding
• Specific rather than abstract modelling
• Many models rather than one
• Continual re-modelling
• Lots of ‘FAT’ data rather than thin data-
fitting
• Multiple data validation – multi-level and
multi-type
• A community rather than individual effort

Conclusions
• We can do some messing with simulations and try
to work out what rules we should institute to
achieve certain goals but…
• ...that will only be of limited use.
• Rather we will have to do much more of looking at
institutions that exist (possibly including those we
initiate) – how they work in practice
• Continually re-modelling to understand how they
are working (or not), given the cultural and
contextual backgrounds in which they work
• Continually intervening to adapt the outcomes

The End
The Centre for Policy Modelling:
http://cfpm.org
These slides will be available at:
http://slideshare.net/BruceEdmonds

Towards Institutional System Farming

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