Humans, Automatons and Markets

1. Humans, Automatons and Markets Shyam Sunder Yale University Center for Analytical Research in Technology Tepper School of Business, Carnegie Mellon University October 10, 2007

2. An Overview • Until recent decades, economics had been constrained by • Mathematical solvability of models • Controlling or observing strategies that generate empirical observations • Computers allow us to simulate complex markets and specified strategies • Confluence of psychology, computer science, and economics now allows us to: • Engineer alternative models of trader behavior and map them to market outcomes • Find out if, and to what degree, the limited human cognition stands in conflict with market equilibria derived from agent optimization • Design markets with specified outcome properties through study of statistical interactions among traders • Examine investor attempts to gain competitive advantage through algorithmic trading Humans, Automatons and Markets (c) 2007 Shyam Sunder

3. Designing Trading Automatons • Psychologists have long questioned the validity of economic theories predicated on optimizing the behavior of agents • Computers allow us to populate markets with various kinds of models (including psychological) of individual behavior, and to observe their aggregate level outcomes • Simulations allow us to assess the strengths and weaknesses of human vs. automaton traders, and to engineer hybrid strategies that might combine their strengths Humans, Automatons and Markets (c) 2007 Shyam Sunder

4. Human Traders • Simon: proposed and empirically validated a coherent theory of intuitive human behavior • Bounded rationality as a theory of how the mind works, and not optimal costly search • Economics, decision theory, psychology, and sociology inform us about trading motivations, opportunities, information, cognition, and learning • Trading complexity limits individually optimal decisions through hot intuitions • Reading, instruction, and experience can help modify beliefs about opportunity sets, behavior of others, interrelationships among variables, and response and outcome functions in a market setting • Human learning by experience tends to land on a plateau • Can cyborgs help? Humans, Automatons and Markets (c) 2007 Shyam Sunder

5. Can Cyborgs Help? • What are the comparative advantages of human and automaton traders? • The boundary between computer and human traders has become less clear • To what degree can social, cognitive and brain sciences inform the engineering of automatons and their work with humans • Parallels from other fields: birds’ flight, internal combustion engine, car, electricity, chess • There appear to be only a few useful parallels • Combining human advantages (abstraction, pattern recognition, hypothesizing, robustness, versatility and imagination) with automaton advantages (fast in simple steps, large memory, and repetition without getting tired, bored, discouraged or frustrated Humans, Automatons and Markets (c) 2007 Shyam Sunder

6. Cognitive Limitations of Humans • How relevant is the study of these limitations to the design of trading automatons? • May help design traders who take advantage of their human counterparties • However, building such limitations into automatons would defeat the very purpose of building better traders • Difference between doll-making and engineering • Building automatons that imitate human limitations may have scientific value, but their engineering value is unclear Humans, Automatons and Markets (c) 2007 Shyam Sunder

7. Demand for Trading Automata • Traders in every market strive to gain competitive advantage through every available means (Reuters, trans-Atlantic cable) • ICT has enabled detailed instructions to implement complex, state-contingent, even learning strategies, and communicate them over long distances for rapid execution at remote servers • Four environments for trading automata: • When optimal behavior of trader is specified by theory as a simple function of information available to trader: faithful execution without errors and elegant variations, e.g., Vickrey auction • Optimal strategy is known but is computationally demanding, e.g., arbitrage trading to “dredge the pennies” • Optimal strategy is unknown calling for execution of heuristics, progressive revision, and learning from experience, e.g., DA • Mutual dependence of expectations and strategies with questionable existence and uniqueness of equilibria: most difficult environment for automata Humans, Automatons and Markets (c) 2007 Shyam Sunder

8. Does the Knowledge of Equilibria Help Design Automata? • Auction theory literature focuses on identifying equilibrium trading strategies and their market level outcomes (Nash) • When Nash does not exist, there is no obvious candidate strategy • The designer might make a guess about others’ strategies and design a good response • Or an automaton may be designed to find the best response to the designer’s beliefs about others’ strategies • Or the designer may have automata form “beliefs” (how?) Humans, Automatons and Markets (c) 2007 Shyam Sunder

9. Does Nash Help the Designer? • The existence of Nash hardly simplifies the problem of designing automata • Existence insufficient to convince people that others will use them • What should a trader do if he does not believe the others will use Nash? • What should the automata do in off-equilibrium paths? • Is there a way for one to use the knowledge of equilibrium to make money? • In Santa Fe DA Tournament, BGAN performed 10 s.d. under the winner • DA with truth telling attains equilibrium but loses out in a heterogeneous market to non-truth tellers Humans, Automatons and Markets (c) 2007 Shyam Sunder

10. Number of Traders • Theory and experiments establish a rapid asymptotic approach to theoretical equilibria as N increases, 95% with a mere 5-6 traders in DA and some other auctions • Greater the competition  closer to equilibrium  smaller absolute gain from better strategies • Trader’s equilibrium share of surplus is a rent which is likely to be bid away, leaving only the amount earned above the equilibrium level for the trader Humans, Automatons and Markets (c) 2007 Shyam Sunder

11. Is Speed an Advantage? • Speed of computation, decision entry, and awareness of market events are pluses • Speed expands the opportunity set of the trader • Can does not imply should act faster. Long (strategic?) pauses in continuous human trading (not cognitive) • Is pausing a good strategy for automatons? We do not know. Wilson’s (1987) Waiting Game Dutch Auction is the only model of this type • Strategic use of timing requires forming expectations of what others might do and when • Oft-observed flurry of activity before closing in both human as well as automaton markets • In the Santa Fe tournament, more than 50 percent of efficiency losses arose from untraded units among intra-marginal traders • Does the strategic use of timing confer any systematic advantage? How do we learn to have our automatons make such use of time? Humans, Automatons and Markets (c) 2007 Shyam Sunder

12. Cognition and Automata • As calculating machines, humans act by intuition and stripped of their learned algorithms, do not perform well and commit systematic errors, not all of which are attenuated by experience • Automatons should exploit such weaknesses in others when possible, but not be subject to them • Difference between markets with reservation values inherent in traders vs. market dependent values (e.g., stock markets) Humans, Automatons and Markets (c) 2007 Shyam Sunder

13. Learning Strategies • Level zero: the data, initial opportunity sets, rules of the market, and mapping from trader actions and market events to payoff functions to start with • Tempting to include optimal decision rules, but the parameters needed to apply or condition such rules on are rarely available • In some markets (one-shot sealed bid auction) learning does not proceed beyond this basic level and automatons enjoy an inherent advantage Humans, Automatons and Markets (c) 2007 Shyam Sunder

14. Level 2 • Forming expectations about parameters, opportunity sets, and payoff relevant consequences of one’s own actions and of market events • Humans are naturally equipped to do form and adjust such expectations instantaneously without apparent effort • Ill-defined problems rarely stop people from offering answers, even wrong answers; they learn from experience and go on to devise better answers • Fluidity of the human brain is an advantage at this level; better than what machines have been able to do so far • Building Bayesian adjustments in automata still requires endowing them with priors and likelihoods appropriate for the environment Humans, Automatons and Markets (c) 2007 Shyam Sunder

15. Higher Levels • Detecting changes in trading environment • Whether the change is endogenous (learning and behavior) or exogenous • Humans rapidly form, test, and reject many hypotheses • Building automata with this level of learning is a challenge • Versatility of humans beyond unstructured tasks • How will automata do in expectation formation, and in competing against their own clones? Humans, Automatons and Markets (c) 2007 Shyam Sunder

16. Markets Prone to Indeterminacy • In private value markets, prices are determinate and the consequences of one’s actions are known, albeit with noise • In security markets with short term investors, values depend on beliefs we impute to unknown others, and their beliefs  price indeterminacy and bubbles (Keynes’ beauty contest, Hirota and Sunder 2006) • Even if the trader knows the fundamental value, he cannot benefit from trading on that value unless the market prices reflect that value before his investment horizon • Such markets present the most difficult challenges for designers of automata Humans, Automatons and Markets (c) 2007 Shyam Sunder

17. Three Simple Designs • A: Automaton ignores all but its private information and the “fundamental value” based on this private information; buys below and sells above this value • B: Automaton assumes that the next transaction price will be equal to the most recent price • C: Automaton uses all past data to search over a set of forecast functions for price prevailing at the investment horizon and trades relative to this price • No automaton can beat its own clones • Singleton A against many Bs will not beat B • Whether A and B will do well in a market dominated by Cs depends on the set of forecast functions used Humans, Automatons and Markets (c) 2007 Shyam Sunder

18. Is Stationarity a Problem? • It is possible that genetic algorithms or neural networks may come up with occasional winners against some alternatives • What happens in non-stationary environments? • Neural networks need training and data and a stationarity assumption at some level • Will they dominate human traders in nonstationary environments? • Perhaps computer scientists and mathematicians already know the answer Humans, Automatons and Markets (c) 2007 Shyam Sunder

19. Design of Trading Automata • Trade off between the speed and depth of decision making • In a fast moving DA, advantages of deep calculations are erased by obsolescence • Relative, not absolute speed, counts, generating profits for early adopters of fast computers against humans and slower machines • Depth of analysis is a decision of the trader subject to trading environment: automaton should be able to conduct its own Turing test (whether it is trading against other machines, and assess their level of sophistication) Humans, Automatons and Markets (c) 2007 Shyam Sunder

20. What about Market Psychology • Market psychology or animal spirits formalized in economics as higher order beliefs • It has been difficult to build such abilities in automatons (and we are also unclear about how humans form higher order beliefs) • Little theory, evidence, or laws to govern higher order belief formation • Will humans do consistently better or worse than Data (of Star Trek)? Humans, Automatons and Markets (c) 2007 Shyam Sunder

21. Markets for People and for Machines • Humans minimize chances of failure by gradual adaptation of their systems • AURORA of CBT visually reproduces the trading pits – still rejected by traders • Such systems were designed for human traders assisted by computers for input, output, storage, record keeping, communication, and rule enforcement, not for automatons • In a market designed for machines, speed is a pre-requisite, not a choice • Absolute competitive advantage of speed will diminish over time, but the relative advantage will remain Humans, Automatons and Markets (c) 2007 Shyam Sunder

22. To Summarize This Part • Productive use of automata in scientific research • Allow us to fix behavior and explore properties of their environments—useful ceteris paribus approach (difficult for automata to modify themselves, difficult for humans to stand still) • Automata used to supplement humans with speed, memory, and computation (arbitrage) • But dreams are built not on science or labor-saving but on the sci-fi versions of self-learning automatons that can humiliate the masters of the universe on the Wall Street • Whether this can happen depends on which side of the Chinese Room debate you are on • I do not know enough AI to give an answer Humans, Automatons and Markets (c) 2007 Shyam Sunder

23. Economics Suggests • Either there will be one winner which will drive out all others and thus close the market, or there will be no stationary equilibrium among strategies • If competing automatons coexist, only about one half of them will perform above average, just as naïve traders and expert fund managers do • In deeper markets, net returns to investors are about the same whether they use their own naïve random strategies, or pay experts to manage their money • Any extra returns earned by experts are captured by them • Any profits earned by smart automatons, too, will end up in the pockets of their designers • Will having smart traders doing all the trading change allocative efficiency? Humans, Automatons and Markets (c) 2007 Shyam Sunder

24. Importance of Being Intelligent? • Computer simulations reveal the robustness of certain market outcomes, and sensitivity of others, to trader intelligence • These simulations, and the analyses that follow, help address critical questions of why some markets, populated by limited cognition human traders, approximate the predictions based on optimization (while others exhibit systematic deviations from such predictions) • Are social institutions built (or have they evolved) to minimize the importance of our intelligence for their efficiency? • Buy stock in a company that’s so good that an idiot can run it, because sooner or later one will. Peter Lynch Humans, Automatons and Markets (c) 2007 Shyam Sunder

25. Designing Market Institutions • Institutions are defined by their rules • Designing a market consists of specifying its rules so that it yields outcomes with known characteristics under a range of trader behaviors • Computer simulations help us understand how market rules determine the statistical properties of interactions among traders Humans, Automatons and Markets (c) 2007 Shyam Sunder

26. Summary • We can engineer trading algorithms that embody their models and conjectures and map them to market outcomes • Bridging the chasm between psychology (individual behavior) and economics (aggregate outcomes) • Designing market rules with specified outcome properties by study of statistical interactions among traders • Prospects and consequences of investors to build algorithms to try to gain competitive advantage over human and other algorithmic traders • Mostly open questions, few answers yet Humans, Automatons and Markets (c) 2007 Shyam Sunder

27. Thank You! shyam.sunder@yale.edu www.som.yale.edu/faculty/sunder