Shared Terminology in Social Science Using Multi-Agent Simulations

1. Shared Terminology in Social Science Using Multi-Agent Simulations Dante Suarez Trinity University Lake Arrowhead, Human Complex Systems, 2007 This presentation includes work done in conjunction with professors at Trinity University: Yu Zhang & Mark Lewis, Computer Science, Christine Drennon, Sociology and Anthropology, Claudia Scholz, Research Programs Coordinator, and Luis Schettino, Psychology.

2. Linear vs. Nonlinear Science • Most science until now is based on linear models. • Linearity is based on independence of agents. • Under an assumption of linearity: The whole equals the sum of its parts.

3. Nonlinear Science • Agents are interrelated. • You cannot reduce the system. • Solutions to the system are complex: the algorithm is the shortest solution. Emergence: The whole does not equal the sum of its parts.

4. Emergence • If emergence exists, then I expect to encounter a world full of emergence. • A world of….my favorite word, levels. • A reductionist approach is insufficient.

5. Bottom’s Up! Modeling atoms • In Economics, society does not exist. • A paradigm based on the individual: an exogenous entity. • All behavior is the product of current agents. • The Utility function represents the individual, and it is set to reflect her preferences.

6. Modeling Individuals in a Complex System • Societies are inherently complex, but yet individuals are still simple. They are still composed of little selfish ‘agents’. • The ant of Herbert Simon. • However, somehow we must model them with the right assumptions so that their aggregate reflects its true complexity. U( x, y) Ui(xi, yi, xj) or Ui(xi, yi, Uj) • There has to be more to it. The utility functions of the individuals themselves are interdependent, changing, and at some level a function of the actual aggregate behavior.

7. Structure • A reductionist approach is insufficient. • Models with Low-Cognition Agents. • Endogenous utility functions. • The agent represents a ‘level’: bounded and binding.

8. Ui Realm of Action for the Utility function Realm of Action for the Utility Function

9. Whose Utility Am I Maximizing • I maximize the utility of the one I am today. • I maximize the utility of all my “I’s”.

10. Patterns of Behavior Aims Resulting behavior over time Intertemporal aims Resulting force t1 t2 t3 t4 t5 A. The static decomposition of desire B. The intertemporal decomposition of utility

11. Whose Utility Am I Maximizing • I maximize the utility of the one I am today. • I maximize the utility of all my “I’s”. • I maximize the utility of my family, of my group, of my country, of my species. • The lines of who are my own, who is not, and who are my enemies are not drawn, but are in constant change. • We must recognize the self interest of groups. • Relative objects and subjects.

12. Back in to Utility • Individuals are another one of the many levels the maximization is happening at. • In lower levels, individuals are a composition of inter-temporal maximizations. • To form higher levels, sets of individuals may decide to join and form groups that maximize their joint utility. • These sets of individuals, or of other subsets, have the incentive to become cohesive and coordinated. • Environmental Optimality.

13. The Hedonist vs. the Calvinist • I believe there is evidence showing that warmer climate societies tend to be poorer. • Selfishness vs. Altruism / Coordination vs. Autonomy Agent’s Productivity Retirement-fixated Myopic Hedonist

14. Individual’s Utility Behavior Collective Force A hierarchical decomposition Uf Us Self Uc Family Country Ui Instinct Upper levels’ realms of action Lower levels (emotions, instincts)

15. A World Divided into Levels • The agent in the model represents a level of: • Strategic decision making. • The evolution of adaptability and responsiveness. • It confines the subcomponents that belong to it. • It is constrained by the upper level it belongs to. • The upper levels may be created by a conscious decision or by an evolutionary force. • Upper levels can represent coordination, identification with others, institutions, implicit laws, religion, a credit bureau. • The lower levels seemingly disappear as the subcomponents become more coordinated.

16. A Language to Describe Social Behavior • Levels related to emergence: Disciplines. • Scalability. • FUZZY AGENTS.

17. Fuzzy Agents The United Stated and Europe share a historical alliance, but may struggle for world leadership NAFTA Joins Canada, USA and Mexico The European Union creates a more cohesive agglomerate Hispanics in USA Latina Americans share culture among themselves

18. What is Science About? What I learned in School… • Look for models with a unique equilibrium. • Karl Popper: Predict the Future, no matter the assumptions.

19. Is this Science? • Chaos (sensitivity to initial conditions), Complexity (irreducibility: the algorithm is the shortest answer). • A world with multiple equilibria. • Assumptions matter: Our theory explains, it is descriptive.

20. Can we do this? / Validation • I can write my name with enough degrees of freedom. • Every degree of freedom we use must be justified by higher predictive power. • Can we make a picture of the world? Can we have a language to paint that picture? • Picture in Picture: A movie. • Connect the dots: History.

21. Optimality in a Hierarchically Decomposed World • To describe reality: a benchmark position can be one in which all behavior is optimal, so long as we identify the true active agent. • Suboptimal behavior is thus a product oftoo much ‘zoom in’ or ‘zoom out’.

22. Reward Functions • The Environment has something that the agents want; it provides it when they act in a certain way, which may involve competition and cooperation. • The sugar of sugarscape. • Hansel and Gretel. • Sugar is Free Energy. • Sugar multiplies with cooperation.

23. Current Work / Applications • The imposition of a Geographical Citizenship/Nation State. • The Social Value of a Micro Loan. • Measuring the Optimality of a Social Structure.

24. Editing Agent Arousal Attention Expectation KB Motivation Social Constraints Tone of decision-making Evaluation Perception Modes of Decision-Making NADIA* (Neurologic Activity Driven Intuition-based Agent) Intuition Deliberation communication Alternatives Satisfying Decision-Making message action Visualization Interface Environment

25. NADIA* • Each element in the Utility function should be reflecting the interaction with the levels or ‘creatures’ of our world. • Positive and negative feedbacks (reinforcement and dampening effects). • Perception or Cognition of levels. • Real vs. perceived probabilities. * These slides represent work done in conjunction with professors at Trinity University: Yu Zhang & Mark Lewis, Computer Science, Christine Drennon, Sociology and Anthropology, Claudia Scholz, Research Programs Coordinator, and Luis Schettino, Psychology.

26. Our World • What are the Physical, Chemical, Biological laws of our world? • Does each level have it’s laws (the level as a dimension). • Interconnections between dimensions. • Exchange Rates (which are not necessarily linear).

27. Economics and its Realm of Action • Pareto Optimality: The optimum you get to where there is no way of making anyone happier without hurting someone else. • Pareto optimality represents the core of Neoclassical economics’ welfare theorems. • Revealed Social Preference. • Do developed countries want poor ones to become wealthier? Do the rich care about the poor? • Evolution ‘disentangles’ agents: The Geneva Convention

28. An Example: Mexico’s Dilemmas • 18th Century Mexico’s GDP higher than U.S.’s. • Weak upper level. • Rule following. • Niche production and supply. • Colonial lock-in. • The ruling class in control. • Democracy without issues. • Lack of well established judicial norms. • Part of the solution: Localized taxes & Federalization.

30. The Business of Violence • The model should classify conditions for competition vs. mutualism. • Agents can be nasty. • Where does war fit? • Is it competition that is in some sense optimal or a bad case of the prisoner’s dilemma? • What about the world economy? • Income distribution?

31. Individual vs. Group Selection • The ‘Reductionism Nightmare’. • Genes do not make sense by themselves. • Your children need a decent ‘pool of genes’ for them to preserve the species. • Individuals cannot survive by themselves. • Optimal species’ scales of environment exploitation. • Many small and malleable individuals in species vs. large organized wholes.

32. I. Introduction to altruism

33. +

35. Delayed benefit Fig. 10

36. Fig. 11

38. How can altruism evolve? Group-level selection Individual-level selection: Kin selection Reciprocal altruism

39. Group selection

40. Group selection

41. How can altruism evolve? Group-level selection Individual-level selection: Kin selection Reciprocal altruism

42. II. Reciprocal altruism Opportunity for reciprocation of altruistic acts Repeated interactions between the same individuals Many opportunities for altruistic acts Altruists interact in symmetrical situations Individuals have good memories Cheaters are punished Robert Trivers (1971)

43. Game theory The best (optimal) strategy depends on what others are doing Evolutionary stable strategy (ESS): resists invasion from other strategies

44. R S T P Fig. 14 Prisoner’s Dilemma T > R > P > S

45. Iterated prisoner’s dilemma Opportunity for reciprocation of altruistic acts Repeated interactions between the same individuals Many opportunities for altruistic acts Altruists interact in symmetrical situations Individuals have good memories Cheaters are punished Tit for tat strategy won

50. III. Kin selection Inclusive fitness includes: • Direct fitness (own offspring) • Indirect fitness (additional reproduction of relatives due to help of actor) W.D. Hamilton (1964)