Humans, Automatons and

imminentpoppedAI and Robotics

Feb 23, 2014 (4 years and 1 month ago)


Humans, Automatons and

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


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

Confluence of psychology, computer science, and economics now
allows us to:

Engineer alternative models of trader behavior and map them to market

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


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


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

Trading complexity limits individually optimal decisions through hot

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


Can Cyborgs Help?

What are the comparative advantages of human and automaton

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


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


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

Humans, Automatons and
Markets (c) 2007 Shyam Sunder


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’

Or the designer may have automata form “beliefs”

Humans, Automatons and
Markets (c) 2007 Shyam Sunder


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

What should a trader do if he does not believe the others will use

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


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


Is Speed an Advantage?

Speed of computation, decision entry, and awareness of market
events are pluses

Speed expands the opportunity set of the trader

does not imply
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

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


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


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

Humans, Automatons and
Markets (c) 2007 Shyam Sunder


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

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


Higher Levels

Detecting changes in trading environment

Whether the change is endogenous (learning
and behavior) or exogenous

Humans rapidly form, test, and reject many

Building automata with this level of learning is a

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


Markets Prone to Indeterminacy

In private value markets, prices are determinate and the
consequences of one’s actions are known, albeit with

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

Such markets present the most difficult challenges for
designers of automata

Humans, Automatons and
Markets (c) 2007 Shyam Sunder


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


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


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


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


Markets for People and for

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

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


To Summarize This Part

Productive use of automata in scientific research

Allow us to fix behavior and explore properties of their

useful ceteris paribus approach (difficult for
automata to modify themselves, difficult for humans to stand

Automata used to supplement humans with speed, memory,
and computation (arbitrage)

But dreams are built not on science or labor
saving but on the
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


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


Importance of Being Intelligent?

Computer simulations reveal the robustness of certain
market outcomes, and sensitivity of others, to trader

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

Are social institutions built (or have they evolved) to
minimize the importance of our intelligence for their

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


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



We can engineer trading algorithms that embody their
models and conjectures and map them to market

Bridging the chasm between psychology (individual
behavior) and economics (aggregate outcomes)

Designing market rules with specified outcome
properties by study of statistical interactions among

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!