Information Markets & Decision
Makers
Yiling Chen
Anthony Kwasnica
Tracy Mullen
Penn State University
This research was supported by the Defense Advanced Research Projects Agency (DARPA) and the Laboratory for Economic
Management and Auctions (LEMA)
1
Two Features of Information Markets
1. Information is revealed over time.
– As security maturity approaches better
information becomes available.
2. Information provided by the market can be
used to improve decision making.
– If the market is aggregating information, a
decision maker can treat the market as a new
source of information.
2
Timing v. Decisions
There is a potential tradeoff between information
refinement and decision making.
–
Delay is costly. Decision makers often prefer to make
decisions earlier.
•
•
–
It is more expensive to move resources quickly.
Low cost options can disappear.
If delay is costly, a decision maker must decide
between making a decision earlier with less accuracy
but low cost and a decision later with higher accuracy
but high cost.
3
This Research
We examine experimentally the interaction
between decision makers and information
markets when delay is costly and new
information is revealed over time.
– What is the optimal decision making strategy?
– What do human decision makers do?
• Do decision makers make systematic errors when
utilizing information market data?
– How does the information market perform with
multiple decision makers?
4
Experiment Design
• Two states of the world: Good, Bad
– Prior probability of Good and Bad is .5
• One state contingent asset:
ìïï 100 if Good
vA = í
ïïî 0
if Bad
5
Experiment Design (cont’d)
• 8 traders endowed with 5 units of the
asset and cash.
• Can buy or sell in a standard asset
double auction market.
• Each auction period consists of 5
discrete rounds lasting 3-4 minutes.
• 5-6 auction periods per experimental
session.
6
Trading
• Traders can buy or sell units of the
asset.
• Traders are given an informative signal
about the likelihood of the two states.
– Signal g is more likely in Good and signal b
is more likely in Bad.
– Observe one new signal in each round
7
Trading
Since traders observe an additional signal at the beginning
of each round, the information in the market improves
over time.
Example of how information might change overtime
g
b
Pr(G)
Pr(B)
1
1
0
.6
.4
Round
2
3
4
2
2
3
0
1
1
.69
.6
.69
.21
.4
.21
5
4
1
.77
.23
8
Decision Making
All traders must make a decision (1 or 2)
– The value of the decision depends upon
the state (Good or Bad).
– There is a cost c associated with delaying
the decision (to later rounds).
– Decision must be reached before
resolution of the state.
9
Decision Making
Decision 1 is preferred
when the state is
Good and 2 when
the state is Bad.
All payoffs decrease
by c for each round
of delay.
Decision Payoffs
Good
Round
Bad
1
2
1
2
0
550
50
50
550
1
540
40
40
540
2
530
30
30
530
3
520
20
20
520
4
510
10
10
510
5
500
0
0
500
10
Market Timing
NO INFO
1 SIGNAL
2 SIGNALS
3 SIGNALS
FULL INFO
DM0
Note: DM only participate in latter DM stages
if they did not make a decision earlier.
Decisions cannot be reversed!
Trading
Round 1
DM1
Trading
Round 2
DM2
Trading
Round 3
DM3
11
Treatments
Two Treatment Variables:
1. Signal strength
•
•
Low Information Signals (Low)
P(g|Good)=P(b|Bad)=0.6
High Information Signals (High)
P(g|Good)=P(b|Bad)=0.8
2. Presence of a decision maker
•
•
All 8 traders are DMs (DM)
No traders are DMs (noDM)
12
Treatments
Sessions Completed
Decision Makers
DM
noDM
Low
3 (24)
2 (16)
High
3 (24)
Signals
8 traders per session for 5-6 periods
All sessions conducted at the LEMA lab at Penn State University.
13
Market Performance Hypothesis
• The Fully Revealing Rational
Expectations Equilibrium (FREE) will
result in a price equal to the aggregated
probability (times 100) of Good
occurring given all the signals.
• Alternatives:
– Private Information
– Uniformed
14
Predicted Decision Making
Signal treatments
affect the timing of
decisions.
–
–
–
Predicted Distribution of Decisions
0.9
0.8
0.7
No decision in
round 0.
80% of decisions in
round 1 under
High.
Expected round of
decision: 1.2 (High)
v. 2.8 (Low)
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
3
4
5
Round
High
Low
15
Decision Making Results
Result: Decisions
often consistent
with optimal
decisions.
– 71.2% of all
decisions are as
predicted by
theory.
Proportion of Choices Predicted
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
2
3
4
5
6
Period
Treatment High
Treatment Low
16
Decision Making Results
Result: Timing of decisions is rarely
consistent with optimal decisions.
– 12.1% of all decisions are placed at the
time predicted.
– Mean Decision Time: 2.02 (High)
1.83(Low)
• Significantly different from predicted: t=5.49
(High) t=5.65 (Low)
• Direction is flipped.
17
Decision Making Results
– 34.5% in High and
44.5% in Low
– This is never
predicted by the
theory!
Timing of Decisions
Frequency
Result: Decisions are
often made before
any information is
acquired.
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
1
2
3
4
5
Round
High
Low
18
Decision Making Results
Result: There is no
evidence of
learning.
• High is consistently
later than predicted.
• Low is consistently
earlier than
predicted.
Average Distance from Predicted Time
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
1
2
3
4
5
6
Period
High
Low
19
Market Data Example (Low)
Market Price v.s. Theoretical Price
LOW SIGNALS
120
100
Price
80
60
40
20
0
Trading Period
Market Price
Theoretical Price
20
Market Data Example (High)
120
Market Price v.s. Theoretical Price
HIGH SIGNALS
100
Price
80
60
40
20
0
Trading Period
REE Price
Trading Price
21
Market Data Results
Result: The market does not fully aggregate
information.
– Average Absolute Difference from FREE price:
40.8(High) 27.9(Low).
Result: There is evidence that the market
follows the information revealed.
– Rd. 1 Avg. Absolute Difference: 47.6 (High)
– Rd. 5 Avg. Absolute Difference: 28.2 (High)
Result: The presence of a DM does not affect
market performance.
22
Explaining non-optimal decisions
Private Information: DMs ignore the market and
base decisions only on their own private
signals.
• Predicts no decisions in round 0.
• Predicts longer waiting for decisions:
– Mean decision round: 2.50 (High) 2.94 (Low)
• Still statistically significantly different than observed
means. (t=3.20 (High) t=6.44 (Low))
• Implication: Any model that predicts some
incorporation of market information will
predict no decisions in round 0.
– Will not fit 40% of observed decisions
23
Explaining non-optimal decisions
Hedging: Since DMs are also Traders,
they can hedge by taking opposite
positions in the markets and decisions.
– Example: A DM selects action 1 (which
pays off in the Good state) and sells all
their assets (which pays off in the Bad
state).
– Might explain round 0 decision making.
24
Explaining non-optimal decisions
Hedging: Classify a subject as hedging in
a given period if they make decision
counter to their strong (at least 5 units)
buying or selling habits.
• Proportion of obs. classified as
Hedging: 19.8% (High) 14.8% (Low)
– Many observations do not involve
decisions in round 0. (41% and 37% in rd.
0).
25
Other Decisions
Remove all 0 round decisions and look only at those that may be
consistent with the theory.
• Average decision round now later than
predicted and treatment effect now closer to
expected.
Avg. Decision Round: 3.09 (High) 3.30 (Low)
t=15.8
t=2.86
– Mean decision round in High treatment appears to
be declining with experience.
– 80% of decisions as predicted by theory.
– 20% of decision times as predicted by theory.
26
Conclusions
• Human decision makers regularly decide to ignore
all potential for added information when the
information is coming from a market process.
• Contingent upon making an informed decision,
human decision makers generally delay too long.
• The information market prediction is not negatively
affected by the presence of decision makers.
27
Next Steps
• Additional treatments to understand decision
making biases.
– Decisions without the market but same info.
– DMs and Traders are different.
• Understanding Market Dynamics
– What does the convergence process look like?
– Even if decision makers to do the right thing, added
uncertainty comes from the market adjustment process.
28