Presentation on theme: "Computational Approaches to Economic Valuation & Strategy Choice Colin Camerer Antonio Rangel Caltech."— Presentation transcript:
Computational Approaches to Economic Valuation & Strategy Choice Colin Camerer Antonio Rangel Caltech
Outline Brief history of the role of computation in Economics Models of valuation and simple choice (Rangel) Models of strategic choice and learning in games (Camerer) Computational issues at different levels: individuals, firms, markets (Camerer) Future directions of research
I Brief history of the role of computation in Economics
Computation is at the heart of economic problems Consider some typical problems: Individual: What snack should I pick out of the buffet table? Individual: Optimal investment portfolio? Firm: Price setting and production selection problem Market system: price formation Thus, one would expect computational based models of decision- making to be common in Economics
This is not the case: Traditional deliberate ignorance of computational detail The triumph of “as if” modelling (economic behaviorism) -- Pareto (1987): “Pure political economy has therefore a great interest in relying as little as possible on the domain of psychology” -- Friedman (1953) Test predictions of theory rather than realism of assumptionsà can ignore computational detail Fictional stand-ins for computation - Walrasian auctioneer - equilibrium in games Underlying computational processes are modeled in REDUCED FORM
Traditional view (cte.) Axioms are considered primitives (logic vs biology as constraint on choices) A developed preference for general mathematical proof over simulation –Study of “procedural rationality” algorithms (Simon) did not gain traction –Distrust of complicated many-equation macroeconomic models & simulations –Little post-1990 taste for SFI agent-based modeling
Neuroeconomics: neurobiologically based computational models of decision-making Goals of Neuroeconomics: 1.What computations are carried out by the brain to make different types of economics decisions? 2.How are these computations implemented by the brain? 3.What are the implications of this knowledge for economics, finance, education, AI, marketing, … ?
Computation is at the core of Neuroeconomics COMPUTAT. MODELS JUGDMENT & DM NEUROSCIENCEPSYCHOLOGY ECONOMIC APPLICATIONS THERAPEUTIC APPLICATIONS BUSINESS APPLICATIONS A.I.
II Neuroeconomic Models of Valuation and Simple Choice
Example I: Reward Prediction Learning Event: trial 1 trial 2 trial 3 … Time: 1 2 3 4 5 6 7 8 9 10 11 12 …. Cue wait Reward Cue wait Reward Cue wait Reward Brain’s problem: learn to predict size & timing of rewards that follow each type of cue Temporal-difference learning algorithms have been designed in CS to solve this problem (Sutton & Barto (1998))
How can the brain learn the reward function? Notation: - True value of state s: mean r(s) - p t (s) = computed predicted value at beginning of triat t (= brain’s best guess about the state’s true value) - t (s) = r(s) - p t (s) = error signal in trial t This error term is extremely important: it serves as THE teaching signal!
Learning Algorithm Step 1. Arbitrarily initialize the decision values p 1 (s) for all s Step 2. Every trial t: -- begin with p t (s) -- measure actual reward -- Compute error (t) -- Update the DV for a and c active in trial as follows: p t+1 (s) = p t (s) + (t) where -> (0,1) is a learning rate Under very general conditions, E(p t (a|c)) -> E(r(s)) for all s
How well do TD algorithms describe brain’s reward learning? CueReward TD-Errors Before Learning TD-Errors During Learning TD-Errors After Learning if Unexpected Omission of reward
Single unit recordings from VTA dopamine neurons revealed that these neurons produce responses consistent with TD - learning: Can we find evidence of TD-error signals in monkeys’ brains? Schultz 
R +6+3 +54 -6 -30 -3 p<<0.001 What brain areas show activation that correlates w/ TD-error signals in humans? From O’Doherty et.al.  CS+ trials at time of CS
Example II: Role of Visual Attention in Simple Choice ?
Model: Three Parallel Processes Visual attention DVs computation Comparator g(e)= L,R e=time elapsed since beginning of choice trial d L (e), d R (e) Choose L,R,or wait
Visual attention DV Computation Comparator g(e) d L (e), d R (e) choose g(e) or switch
Visual attention process First fixation: Stochastic bottom-up process P 0 = Prob first fixation to L Exponential latency: Pr(First fixation begins at t)= 1- B.e - t Subsequent fixations: top-down process Follow the commands of the comparator process
Comparator process: During each fixation, the comparator either chooses g(e) or sends a signal to the visual system to switch gaze Length of each fixation stochastic: -- d = duration of current fixation -- Pr(comparator evaluates at d)= 1- A.e - d Decision made as follows: -- r x (e) = d(t x (e)) - d(t y (e)) -- Choose g(e) with probability -- Wait (and switch fixation) with prob Always switch after first fixation
Model predictions Behavioral: S-shaped choice probabilities Process: RTs and #saccades increase with choice difficulty Performance: - Importance of first fixation: P(choice=best|fist-fixation=best)> P(choice=best|fist- fixation=worse) - First look bias: for items with similar value P(choice=L|fist-fixation=L)> P(choice=L|fist-fixation=R) - …
Test Present until a choice is made Enforce 2000 ms fixation +++ 1000 ms Collect eye-fixations @ 50 Hz
Summary Computation is at the core of the nascent field of Neuroeconomics Goal is to (1) describe the computation and processes that the brain uses to make decisions and (2) establish their neural basis Test the computational processes directly using modern neuroscience and psychology tools -- from fMRI to eye tracking Feasibility of the research agenda has already been proven Novel insights into DM are already being generated by this class of models.
III Models of Strategic Choice & Learning in Games
Some theoretical interest in computational models Finite-state automata (Rubinstein, Neyman, et. al.) Computational complexity (Gilboa-Zemel on NP-hard games) Not linked to data or practical problems
Cognitive hierarchy models of limited strategic thinking Selten (1998): –“The natural way of looking at game situations…is not based on circular concepts, but rather on a step-by-step reasoning procedure” Cognitive hierarchy –“Level 0” use a heuristic (e.g. randomize) –“Level k” best-respond to choices of level 0-(k-1) –Axiom f(k)/f(k-1) 1/k (k-th step increasingly difficult) f(k)=e /k! (Poisson) –Limit as often converges to equilibrium –Simpler than equilibrium in some ways easier to compute predictions no problem of multiple equilibria
E.g. “P-beauty contest” (Ho et al AER 98) pick x in [0,100], x closest to (2/3) of average wins
“Choosing” computations are different than “belief formation” computations Bhatt-Camerer GEB 2005
Field application: “Cold opening” of movies (unavailable to critics for Friday review) Studios do not let worst movies get reviewed… ”cold” opening increases box office
EWA learning in games: Generalized reinforcement Reinforcement, fictitious play linked (Econometrica 99) Update attractions to strategy j from payoff A i j (t) - A i j (t-1) = [ * π (s i j,s -i (t)) -A i j (t-1)]/( ϕN(t-1)+1) = prediction error/ increasing weight is “imagination” of counterfactual payoffs ϕ is recency weight Typical values: N(0)=1, ϕ=.8, weights go from.56 .20 Can replace , ϕ with “ self-tuning ” functions (JET ’ 07) Can add “sophistication”– players know others are learning (JET 02, GEB 06)
Example: Price matching with loyalty rewards (Capra, Goeree, Gomez, Holt AER ‘99) Players 1, 2 pick prices [80,200] ¢ Price is P=min(P 1,,P 2 ) Low price firm earns P+50 High price firm earns P-50 What happens? –Theory: competition drives prices to 80
IV Computational Issues at different levels: individuals, firms, markets
Levels of computational modelling in economics Individuals (what you’ve seen) Firms –Firms as hierarchies of imperfectly informed individuals (Radner-Van Zandt) Optimal hierarchies for aggregating formation Mechanism design –Computability as an individual rationality constraint (Ledyard) Markets –Markets as computational mechanisms Computing equilibria (Judd, Kearns et al) –Smart markets: Hybrids of bids and optimal combination (e.g. combinatorial “package auctions” e.g. PCS spectrum) –Information aggregation Markets ‘compute’ probabilities of events (e.g. prediction markets)
Prediction markets Began with basic research: 20 yrs to wide use Plott and Sunder (1982 Econometrica): Markets for “contingent claims” Pay $1 if an event occurs. Prices reveal probabilities Markets are $-weighted opinion polls of self-selected respondents Iowa Political Markets 1988 (http://www.biz.uiowa.edu/iem/) Markets for political events predict surprisingly accurately Tradesports 2002 (http://www.tradesports.com/) et al Used by some companies, policy markets See Wolfers & Zitzewitz J Econ Perspectives 04
Six hours earlier (9pm EST Oct 26 ‘06): Guess about Karl Rove non- indictment appears in Intrade price drop…36 hrs before Oct 28 Libby indictment BID QtyPrice 125.1 1025.0 124.6 124.4 124.2 123.7 123.6 323.4 1123.3 2723.2 1623.0 1322.5 1022.0 2021.0 1120.0 ASK PriceQty 29.91 30.03 31.91 32.02 32.72 33.010 34.94 35.020 39.05 40.011 50.010 68.85 70.099 72.04 74.91
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V Future of computational models of decision in Economics
@ the Individual level It will look like theoretical neuroscience Focus on modeling the neural and psychological processes involved in decision-making Modeling constraints provided by neural, psychological, and behavioral data Models will be tested with techniques such: - fMRI - electrophysiology - TMS - eyetracing - behavioral predictions
@ the firm and market levels Will build on the properties of the individual level models Model the interactions of many agents Goal will be to improve our understanding of: - auctions - price formation in markets - financial markets dynamics - macroeconomic performance and policy
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