Boxing Futures 4 strategies (and simulation challenges) to deal with the future and its boundaries
Nils Ferrand UMR G-EAU Cemagref
kNOw future? Question 1: How can social simulations include an explicit (symbolic, modelled) representation of the future (social) state in the present decision dynamic? Question 2: Which social protocols for modelling and simulation can impact on social change and divert from forecasted futures? Question 3: Considering the uncertainties, which simulation methods can at least “bound” and shape futures? Tackle the futures' issues with social simulation processes 2
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A wider scope for social simulation All methods and tools aiming at representing and exploring human social activities in controlled and reproducible settings to understand their properties, and assess their responses to triggers – Computer based social simulation – Role playing games – Social experiments
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4 issues, 4 strategies Assessing future responses to new policies when they are socially constrained and stakeholders' driven – 2 level interactive simulations for policy design
Supporting integration and implementation of plans – Participatory planning, procedural design and simulated implementation
Assessing how Futures' Visioning change behaviours – Vision-Age
Toward viable governance – Experimenting viability for governance processes 4
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Application domain: socio-environmental governance
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S1: Let's meet and mix futures Policy makers need to anticipate how citizens would respond collectively to their candidate policy options, negotiate and choose one Citizens need to know how others will react, and which options they are offered, to decide and act for themselves → They can also react directly toward policy makers (elections)
A coupled process, incremental in democratic systems 6
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“heavy” simulation vs. participatory approach Simulate populations responses using data, surveys, social interactions models +/- simulate policy-making (game-theory, deliberation, normative reasoning, constraint satisfaction...) – Couple ???
OR Use interactive simulation (skip one of the simulations) OR Use participatory experiments (dialogue as simulation) – Together : the “shared room” – Sequence : 2-level processes 7
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The “shared room” approach A real (physical) place where policy makers and lay people (target groups) meet on an equal procedural base to discuss, co-design and change
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2 level-process for CC policy integration and testing in Spain (Manez, Ferrand, 2009)
Valenciana Region
SPAIN Integration and testing of multi-sectorial climate change policies for 20/09/11 mitigation and adaptation
PEER Initiative – Partnership for Regional European Environmental Research Individuals
c a s e p r es ent a t i ons
Two-level process Citizen group
Policy group
1 Interviews at national and basin level on responses to climate change scenarios 2
3 Discussing responses to the proposed policies 4 Final policy coherency
20/09/11 (Mañez and Aix, 2008)
Proposing new integrated policies
Setting the content through interviews Discussing behaviours Confronting to policy makers Policy testing under different climate scenarios
S2 : Let's organize a future Integrated Planning + Procedural Design + Implementation – Assess (simulate) combinations of actions and check feasibility and efficiency (what) • Includes social responses (again) and socio-environmental impacts
– Design implementation procedures based on institutional engagement (how) and contingency planning – Check it by (participatory) simulation...
Any computer based social simulation required here?
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BGn Floods & Droughts (Ferrand & al, 2007) : options design and plan integration Expectations
Options development
Options categorisation
Visions et preferences Strategy creation and assessment
Causal 12/33 mapping
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An action model
20/09/11
Floods
Impacts on:
Droughts
Infrastructures
Nature
Politics
Agriculture
Industry
Type
Ï ÊB
Options implemented
Households
Cost
Other resources
Needs
Institutions
Citizens
Infrastructures
Time, duration
Actors
others
NGOs
experts
policymakers
citizens
Comments on impacts
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Risky?
Easy?
Hand made complexity management...
Procedural design Design a protocol for the implementation of a new plan or policy • Specifies: • • • • • • • •
Participants / actors / agencies… Roles and actions Sequence of actions Triggers and switches linked to local condition (what-if) Tools, models, methods to be used Regulation, control, litigation in the process Risk control in the process Method for transfer to practitioners
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time
Process Diagram (workflow) (information stakeholders
Farmers Farmers Farmers
Water Board
External actors
NGO
Bank
Extension services
Support) Black board
External tools Excel sheet
Initial info consultation
E
Phase 1
N D
E
E
E
D
Sending back individual decisions
E
Giving back the water allocations Phase 2 N
N
N 16
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Testing policy implementation Bring stakeholders and administrations to test an implementation procedure and assess the supported policy
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Any computer simulation needed? Yes ! For: – Assessing impact of an action / option (technical or not) on a socio-environmental system – Assessing complex integration / combination of options – Assessing extension / dissemination processes – Assessing impact of an implementation procedure – Checking deadlocks and unexpected consequences
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S3: What if Agents share a Future? From Vision-Age (Ferrand, Weisbuch, Oxford Futures Forum 2011) Stage1: Impact of crowding information on crowd behaviour (Helbing 2007) or reflexive simulation (Ferrand 2003): when simulated groups use this same simulation (and bin it...)
Helbing, D., 2007. Dynamic Decision Behavior and Optimal Guidance through Information Services: Models and Experiments - http://ssrn.com/abstract=960219
Stage2: In a society, some agents stop and envision Futures. What happens then? – Hyp 1: Impact of this shared information – Hyp 2 : Impact of the visioning process 19
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Basic Vision-Age model
∀ k ∈[0...∞], X˚ ij T k1=F i X˚ Tk, E˚ j Tk,∅ and X˚ T =X T
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Agents' adaptation to the Future's vision To the information: – Don't care, BAU – Utility based – Enforce / contest scenario – Strategic (game theory, speculative regression) – Relational / image based
To the visioning process: – As social learning: change beliefs, norms, social relationships, risk aversion, uncertainty, balance short vs. long term
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Protocols for Vision-Age Exploration Run a simulation – Vary forcing parameters and record trajectories after time T – Reintroduce it as a new information at time T (with an updated agent model) – Measure deviation from initial trajectory – A reference simulation on common pool resource consumption – Ongoing research – Help and contributions welcome!
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S4: experimenting for viable futures (+ J. Ward, G. Deffuant & Cemagref LISC team)
Ref. Viability Theory and its applications (Aubin, 1991; Aubin, 2011; Deffuant & al., 2011; Alvarez, Barbier, Béné, Bernardo, Bonneuil, Briot, De Lara, Doyen, Durand, Eisenhack, Kropp, Martin, Martinet, Mullon, Quincampoix, Rapaport, St-Pierre, Scheffran, Thébaud,Tichit ...)
Revising 2-level processes as an exploration of viability conditions and viable trajectories in an interactive simulation process – Policy-makers set the state and control boundaries – Other stakeholders drive through the system
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The viability theory Initiated by Jean-Pierre Aubin (1991) and developed by a larger community in mathematics, economics, demography and ecology « The main purpose of viability theory is to explain the evolution of the state of a control system, governed by nonderterministic dynamics and subjected to viability constraints, to reveal the concealed feedbacks which allow the system to be regulated and provide selection mechanisms for implementing them. » – An alternative to optimization which proposes action only when some limits conditions occur
Aubin, JP, 1991, Viability Theory. Boston : Bikhäuser Aubin, JP, StPierre, P, 2007, An introduction to viability theory and management of renewable resources, in Kropp, 24 J., Scheran, 24/33 J., Eds. Adv Methods for Decision Making and Risk Management in Sustainability Science. NY: Nova Science Pub
The viability concepts (St-Pierre, 2011)
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St.Pierre, P., 2011. Sensitivity indicators and Natural ressources
[email protected] ESSA Conference Montpellier 2011 Montpellier : Cemagref management. InFor « Resilience, Water –and Foresight–»Sept workshop.
In decision terms... Constraints on the state
The set of feasible all controls
Not a single trajectory !!! A set of possible...
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The set of feasible controls for a given state
St.Pierre, P., 2011. Sensitivity indicators and Natural ressources
The evolution of the system
[email protected] ESSA Conference Montpellier 2011 Montpellier : Cemagref management. InFor « Resilience, Water –and Foresight–»Sept workshop.
The viability concepts (St-Pierre, 2011)
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St.Pierre, P., 2011. Sensitivity indicators and Natural ressources
[email protected] ESSA Conference Montpellier 2011 Montpellier : Cemagref management. InFor « Resilience, Water –and Foresight–»Sept workshop.
Viability design experiment (http://viable.labonne.info)
Building the viability model and exploring it with the STH – Setting a situation – Discussing constraints – Exploring and testing trajectories – Comparing with theory (?)
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Wat-A-Game, 2011
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A 2Level simulation protocol on viability 1. PM define the constraints 2. TH play action until – PM pre-alert (through monitoring) – Ressource / wealth / equity crisis • With / without stated boundaries – With / without monitoring !
3. PM & TH (try to) correct – Heavy ? Inertia function ?
4. TH revise power transfer to PM
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Questions between stakeholders and simulation Defining constraints and dynamics – Revising it. When ?
Changing toward a viable model – The model : Viability multipliers • Acting on prices / costs • Acting on the interactions (Aubin, 2004) – Learning !
– Corrections = impulses at the boundary • Viability crisis and intervention
SHAPING FUTURES' BOUNDARIES + NAVIGATING INSIDE 30/33
Aubin, JP., 2004, Regulation of the evolution of the architecture of a network by tensors
[email protected] For ESSAJ.Conference – Montpellier – Sept 2011 operating on coalitions of actors. of Evol. Econ, 13, 2, 95-124
It's Futures' time, now Since Archeomedes (Van der Leeuw & al.), the Anasazi (Dean & al, 2000), or the Balinese temples of Lansing (1991), the past has been a serious challenger and attractor of social simulators – Descriptive analytical posture, reconstruction – Empirical, Fitting data
Very low expectation for predicting the future in general
– Very high uncertainty on social dynamics... especially governed
But there are alternatives to computed predictive simulations when dealing with the future Change is at stake now (or was it since Club of Rome & Limits to growth?) and Science is expected to deliver
Let's put back Building the Future in the social simulation research agenda....
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