Benoit Hardy-Vallée, EHESS, Paris (France) / Université du Québec à Montréal

1. Rational Decision Making As A Unifying Paradigm In Cognitive Science, or Why Animal Are Rationals, And Why It's No Big Deal Benoit Hardy-Vallée, EHESS, Paris (France) / Université du Québec à Montréal

2. On Paradigms Problems

3. Explanations in cognitive science • “identifying mechanisms that produce observable phenomena“ (Thagard 2005) • Marr (1982, see Pylyshyn 1984 also) proposed 3-levels of analysis: • a computational level describing the goal and the function or a mechanism • an algorithmic level describing the procedure • an implementational level describing the material substrate

4. Evolutionary psychology • Tooby & Cosmides (1994 ) • adaptive problem : what is the fit of the process with its environment ? What is the ultimate evolutionary benefits of having this function ? • cognitive program: Which operations the cognitive system (or some subsystem) perfom ? • neural basis: What kind or neural activity realises this process ?

5. Explanations come from theories • Theories are included in larger epistemic structures: • Paradigm (Kuhn) • Research program (Lakatos) • greater unification of science • producing hypothesis • explains best what others theories explained before

6. EP a unifying paradigm ? Problems. • Research conducted under the EP label dealt mostly with human issues • sometimes with primates, because of their close similarity (genetic and social) to us. • “there remains [in EP] a distinct methodological flavour to human research, primarily because people talk” Daly and Wilson (1999: 514) • Researches are conducted with interview data • highly unreliable: imperfections of memory, lies, confabulations • human-centered unity of science • Man at the center of the biological world

7. A genuinely biological paradigm • If psychology or cognitive science is to be a chapter of biology, it should not be a chapter of human biology, but a chapter of biology tout court • If cognitive science is oriented toward a more biological stance, then it must encompass a more general explanatory target and produces generic models of cognitive agency

8. Solutions ? • models of cognitive agency that could apply to human and non-human animals • research program in cognitive science which consider homo sapiens as one animal among many others.

9. A desirable paradigm • A desirable paradigm in cognitive science would have the following characteristics: • applies to all three levels (Marr’s typology) • does not presuppose that one of these levels is fundamental, or more important • encompass human and non-human cognitive agency • is grounded in cognitive science and biology • does not presuppose that one of these science fundamental, or more important

10. Decision Making as a Paradigm in cognitive science

11. Decision theory • Rational agents weigh probabilities and utilities of different possibilities of action, • Make choices according to expected utilities gain. • Decision making is now the basis of consumer theory, microeconomics modelling and decision analysis.

12. decision theory got strategic and interactive • Game theory: • Deciders are players • Decisions are moves in a common payoff structure, analogous to the structure of a two-person game. • Common knowledge: other player are rational, and know the payoff structure • Markets: • many-players, non-zero sum game (both player may win or lose) • Deciders/players are traders • Decision making is an individual, interactive and collective model

13. decision, game, and market theory constitute the the most precise notion and standard of practical rationality • the rationality of behaviour • contrast with theoretical rationality, the rationality of thinking, • standards are spelled out by deductive, inductive, epistemic logic, beliefs revision, etc). • Inside economic theory, it is still the main research program • Even after accommodations: • Herbert Simon’s bounded rationality, • rational agents are not ideal omniscient deliberator but limited being in a constraining world, • Tversky & Kahneman • human shortcoming in economic reasoning, • suggest a model of the economic agent that think and decide using “fast and frugal” heuristics (Gigerenzer)

14. Animal behaviour as biological rationality • Decision theory is now applied to animal behavior • Since the seminal paper of MacArthur and Pianka (1966) Behavioural Ecology treat animals as optimizers or satisficers: • "... which patches [areas] a species would feed [in] and which items [prey] would form its diet if the species acted in the most economic fashion" (MacArthur and Pianka 1966: 603). • (predators are not only carnivores, but every animal that eats another living entity)

15. Optimal Foraging Theory • Decision making for predators • “animals will, as a result of evolutionary selection pressures, tend to harvest food efficiently so if we work out in theory the decision rule which would maximise the animals’s efficiency, these rules ought to predict how the predator makes its choices”. (Krebs, 23: 1978).

16. Sih and Christensen (2001) meta-analysis • reviewed 134 studies (in laboratory and field, experimental and observational) that used OFT as a predictive tool. • The most successful studies has been conducted with • herbivores (Belovsky 1986), • oystercatchers birds (Cayford & Goss-Custard 1990), • fish (Galis &. de Jong 1988, Persson & Greenberg 1990), • hummingbirds (Tamm & Gass 1986) • moose (Vivas et al 1991). • Correlation between theory success and types of prey. • Mobile prey: 37 % fit • Immobile prey: 73.8 % fit. • Normal: decision is a single agent process, games are multi-agents processes

17. Animal Game Theory • Social Foraging Theory • Giraldeau and Caraco (2000) • animals collectively forage and sometimes exploit others foragers. • Research try to predict group size, food intake per individual or producers-scroungers rate. • Lima (2002): Predator-prey interactions • Stamps & Krishnan (1999): Territory establishment and punishment

18. Biological Markets • Noë & Hammerstein (1994, 1995) • Market effects: • A commodity is traded between agents • Competition for scarce ressources • Partner search, selection and switch • Possibility of free riders • Supply-and-demand equilibrium in a multi-agent environment

19. http://www.dimijianimages.com/More-page5-mutualisms/3-way-mutualism.htmhttp://www.dimijianimages.com/More-page5-mutualisms/3-way-mutualism.htm

20. Female baboon: • « Scratch my back, and you can play with my baby » • The more you scratch, the more you can play with the baby • When there is more of babies, the scratching time is shorter

21. The predator does not eat the cleaner fish (a nature prey) Spend more time with occasional « clients » Too much competition for regular clients Cleaner fish

22. Then animals are rational ? • Yes, but it’s no big deal. • Rationality is a behavioral capacity selected by natural selection • Practical rationality emerged before theoretical rationality • Biological market mechanisms structure the agent’s environment • probability and utility can be more easily and directly perceived • computational load get party extended in the environment. • In “The predictive power of zero-intelligence in financial markets”, Doyne Farme et al. (2005) showed that Z.I.T. can mimic the behaviour of the London Stock Exchange with a 96 % accuracy !

23. Other promising avenues • Robotics: (Stentz & Bernardine 1999) • teams of robots implement market mechanism to achieve multi-agent coordination • Neuroeconomics (Glimcher, Schultz) • Research on dopaminergic neurons role in decision making

24. Marr/Tooby & Cosmides typology • Suggestion for the 3 levels on analysis in the decision making research program • adaptive problems • Evolutionary biology, ethology, behavioral ecology, microeconomics • cognitive program • AI, A-Life, robotics, computer sciences, agent modelling • neural basis: • neuroeconomics

25. Biology Decision T. Neuroeconomics Game T. Behavioral ecology Market T. Practical rationality Evolutionary Economics Philosophy Economics Artificial life Agent-based computational economy Market-based multirobots coordination Computer science