Humans, Automatons and

imminentpoppedΤεχνίτη Νοημοσύνη και Ρομποτική

23 Φεβ 2014 (πριν από 3 χρόνια και 4 μήνες)

71 εμφανίσεις

Humans, Automatons and
Markets


Shyam Sunder

Yale University

Center for Analytical Research in Technology

Tepper School of Business, Carnegie Mellon University
October 10, 2007

Humans, Automatons and
Markets (c) 2007 Shyam Sunder

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


Thank You!

shyam.sunder@yale.edu

www.som.yale.edu/faculty/sunder