Changing the Rules of the Game Dr. Marco A. Janssen Department of Spatial Economics

Research questions How do rules emerge, get selected and be remembered in social ecological systems? What can we learn from (computational models of) immune systems and language development?

Contents Puzzles from empirical studies of common pool resources. Immune system Language development Methodology Modeling self-organization of institutions Discussion

Common Pool Resources Are used by multiple-users For which joint use involves subtractability, that is, use by one user will subtract benefits from another user’s enjoyment of the resource It is difficult to exclude users

Management of CPRs Economic Theory predicts Nash equilibrium and overharvesting Solutions to derive cooperative solution: –Government will manage the resource –A market will be created Laboratory experiments and field studies show an alternative: self-organization of institutions.

Factors important for self- organization Type of communication Building up mutual trust relationships Rules how to monitor and sanction defined by the local users and implemented by local users Memory of successful solutions by taboos, rituals, religions, etc.

Immune System Distributed system which is able to detect and eliminate invasions of pathogens. Detection: self vs non-self Response: generation antibodies Memory: storing successful responses

Pathogens Bacteria Parasites Viruses Fungi

Detection

Recognition

Response - Continue generation of new cells. - Replication of cells which bind lots of pathogens: Antibodies - Antibodies neutralize pathogens

Impact of Memory

Artificial Immune Systems Distributed systems for information processes. Origin: –study of immune systems –bio-algorithms: genetic algorithms neural networks

Language development Different perspectives on language. Universal grammar/language: Genetic transmission Localized hard-wired neurological structures: crickets and songbirds Higher animals learn language gradually: training parameters of neural network

Complex adaptive system approach Language: –result of local interactions of language users –self-organizing process –agents benefit from being understood (fitness) –clustering of agent with same language/dialect

Methodology Games: –game theory for institutions, repeated games with prisoners dilemma –language games, imitation games –evolution of grammar: fitness related to mutual understanding

Vowels

(De Boer, 2000) Emergence of vowels by adaptive imitation games

Methodology (II) Networks: –Neural networks: learning by finding the right connection strengths –Immune networks: maintaining immune memory, spreading information over other parts of the network. –Social networks.

Methodology (III) Evolutionary Computation –Genetic and evolutionary algorithms: fitness selection mutation (cross-over)

Modeling self-organization of institutions Coding rules Creating rules Selecting rules Remembering rules

Coding rules Grammar of Institutions (Crawford and Ostrom, 1995) Rules are build up from 5 components: –Attributes (characteristics of the agents) –Deontic: may/must/must not –Aim: action of the agent –Conditions: when, where and how –Or else: sanctions when not following a rule

Creation of Rules Mutations and cross-over Immune systems: constant generation of new lymphocytes Language: interaction with other groups and with new experiences: –Computer led to new words ( & internet) and new meanings (windows & mouse) –Social groups: jargon of scientists

Genetic Libraries

Selection of Rules Rules:Constitutional Collective Operational Levels of analysis: ConstitutionalCollectiveOperational choicechoicechoice Processes:FormulationPolicy-makingAppropriation GovernanceManagementProvision AdjudicationAdjudicationMonitoring ModificationEnforcement

Selection of rules (II) Criteria for success Social networks Mutual trust relationships Recognition of trustworthy others (reputation, symbols, indirect reciprocity)

Remembering Rules Law, universities, taboos, rituals, religions Reinforcement and disturbances Resilience Redundancy

Coverage of antigen space by antibodies

Fitness versus redundancy (Hightower et al, 1995)

Fitness related to redundancy (Hightower et al, 1995)

Training the system Allow small disturbances to maintain training of the strength of the network, the diversity and functional redundancy

Discussion Empirical evidence for self-organization of institutions. Formal models may help to explain observations. But how to formally model how rules emerge, get selected and be remembered?

Discussion (II) We may learn from similarities and differences between institutions, immune systems, and language development. Computational tools exists to simulate immune systems and language development Toward computational laboratories for social-ecological systems.