The cost of irrationality - how poker players perform better by avoiding cognitive biases

Candidate number: 74406 Title: Cognitive biases – the difference between good and
great decision-makers
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Title of Dissertation:
Cognitive biases – the difference between good and great
decision-makers
How poker players perform better by avoiding the availability
and representativeness bias
London School of Economics and Political Science
Management, Organizations and Governance
Course code: MG416
Candidate number: 74406
Date: August 2013
Word Count: 5999

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Cognitive biases – the difference between good and great
decision-makers
How poker players perform better by avoiding the availability
and representativeness bias
Abstract:
Individuals often rely on a limited number of heuristic principles to make judgments.
These cognitive rules-of-thumb can be sophisticated shortcuts for our brain to
approximate the appropriate decision, but they also contribute to consistent and
systematic biases in our decision-making. Consequently academics have argued that
cognitive biases have profound consequences for the quality of our choices in areas
such as politics, management and finance. However, as a rather new academic field,
there has yet to be established conclusive empirical evidence on the extent to which
cognitive biases affect the quality of our decisions. In this dissertation I test whether
the degree to which individuals suffer from cognitive biases have an effect on their
performance in the ultimate test of strategic decision-making, online poker. Poker is a
game of competing agents that make choices based on probabilities, imperfect
information, risk assessments and possible deception – and is thus a good proxy for
decision-making in most strategic situations. Data on 338 players demonstrate that the
two biases tested, availability and representativeness, both are inversely and
significantly correlated to poker performance. Interestingly the biases are not highly
correlated, so their effect and significance increases when they are combined into one
variable. Additionally professional poker players are less prone to both biases than
amateurs, and the data also indicates that cognitive biases are better correlated to
performance amongst this elite group of players. Hence the evidence suggests that
individuals should take measures to overcome cognitive biases in order to become
better decision-makers.
Keywords: Cognitive biases, availability, representativeness, strategic decision-
making, online poker

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Table of Contents
1. Introduction.....................................................................................................................4
2. Literature Review ..........................................................................................................6
2.1 Heuristics and biases - the state of the literature.....................................................6
2.2 Empirical research on the performance implications of cognitive biases ......8
2.3 Poker as a proxy for strategic decision-making........................................................9
2.4 Hypotheses............................................................................................................................10
3. Research Methods.......................................................................................................11
3.1 Sample ....................................................................................................................................11
3.2 Measuring poker performance......................................................................................11
3.3 Individual propensity to the availability and representativeness bias..........12
3.4 Methodology used to test the hypotheses .................................................................13
4. Results............................................................................................................................. 13
4.1 Individual propensity to both the availability and representativeness bias
are inversely correlated with poker performance........................................................14
4.2 Professional poker players are less prone to cognitive biases than amateurs
..........................................................................................................................................................16
4.3 Cognitive biases are more correlated to the performance of professionals
than amateurs.............................................................................................................................17
5. Discussion...................................................................................................................... 18
5.1 Theoretical implications..................................................................................................18
5.2 Practical implications.......................................................................................................19
6. Conclusion and limitations...................................................................................... 21
7. Appendix ........................................................................................................................ 22
7.1 The availability bias ..........................................................................................................22
7.2 The representativeness bias ..........................................................................................24
7. Bibliography .................................................................................................................27
6.1 Additional Resources........................................................................................................35

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1. Introduction
Given the vast amount of decisions we face in our daily lives people often use
heuristics to form judgments (Simon, 1979; Kahneman, 2011). These heuristics are
cognitive rules-of-thumb that help us simplify the complexity of information we face
(Bazerman and Moore, 2008), so that we can efficiently approximate an appropriate
decision (Tversky and Kahneman, 1974; Pitz and Sachs, 1984). However, heuristics
can be problematic since they produce consistent errors or biases in our thinking,
which we usually do not adjust sufficiently for (Hammond et al. 1998, Bazerman and
Moore, 2008).
Evidence indicates that individuals differ in the degree to which they rely on
heuristics in their decision-making process (Neal and Bazerman, 1983; Busenitz and
Barney, 1997). Subsequently some individuals are more prone to cognitive biases
than others, and there have been attempts to understand how these individual
differences affect our decision-making performance (Hammond et al. 1998; Moiser et
al. 1998). Will someone not suffering from cognitive biases make better strategic
decisions than someone suffering from these biases? And if so, how much does it
actually matter? Some argue that the biases simply disappear outside the laboratory
when stakes are high and individuals think harder about their decisions (Gigerenzer,
1991; 1996; 2008; Wright and Goodwin, 2002; Charness et al. 2010), whilst other
believe they can explain variations in strategic decisions (Stumpf and Haley, 1989;
Russo and Schoemaker, 1990; Hilton, 2001; Bazerman and Moore, 2008).
Unfortunately empirical studies on how cognitive biases affect individual decision-
making performance have yet to establish conclusive evidence on their impact
(Fenton-O´Creevy et al. 2004). Some studies demonstrate that cognitive biases are
associated with poor decision-making performance (Grinblatt and Keloharju, 2000;
Shapira and Venezia, 2001; Fenton-O´Creevy et al. 2003; Dhar & Zhu, 2006; Siler,
2010). Whilst other studies find that cognitive biases are equally present with
professionals as with amateurs (Coval and Shumway, 2005; Frazzini, 2006; Chen et
al. 2007)

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To contribute to this debate I study the implications of the availability and
representativeness bias1
on poker players’ online poker success. I have mapped the
individual propensity of 338 poker players to both biases by testing each individual on
a series of 11 decision-making scenarios. By correlating their scores with their online
poker performance, I have analysed how an individual’s propensity to cognitive
biases affects her decision-making performance. Poker serves as an excellent proxy to
real-life strategic decision-making due to its social and strategic nature (Von
Neumann and Morgenstern, 1944; Billings et al. 2002), and consequently my results
generalize beyond the realm of poker.
The results demonstrate that individual propensity to cognitive biases is inversely
related to strategic decision-making performance. Specifically I establish an inverse
correlation between pokerskills and the degree to which one suffers from cognitive
biases. This result is reinforced by results demonstrating that professional poker
players have a lower propensity to suffer from cognitive biases than amateurs. The
analysis also elucidates that avoiding cognitive biases becomes increasingly important
for high-performance individuals, since they often compete against other high-
performance individuals and therefore face slimmer margins of success. Thus I
conclude that cognitive biases impair out ability to make good decisions. Finally I
highlight possible avenues for future research to solidify my conclusions for a greater
variety of biases and decision-making domains (Busenitz and Barney, 1997; Chen et
al. 2007), and offer some brief insights into how individuals should proceed to avoid
these biases in their judgment (Russo and Schoemaker, 1990; Bazerman and Moore,
2008; Charness and Sutter, 2012).
The paper is organized as follows: In section 2 I provide the theoretical framework on
cognitive biases and their potential impact on decision-making performance. And I
outline the specific hypotheses I wish to test. In section 3 I discuss my sample and my
chosen methodology. The results are presented in section 4, before I discuss their
theoretical and practical implications in section 5. Section 6 concludes the paper.
1 For the ease of expression I simply refer to biases stemming from the availability heuristic as
the ”availability bias” and biases stemming from the representativeness heuristic as the
”representativeness bias”…

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2. Literature Review
In this section I outline the theoretical framework I build my hypotheses on. Firstly I
give an overview the heuristics and biases literature - where I argue that it has been
established that cognitive biases affect strategic choices in the real world. Secondly I
argue that even though we know that heuristics and biases affect our judgments in
real-life, sound empirical evidence on how it affects individual differences in strategic
decision-making has yet to be established. Thirdly I argue that poker is a suitable
proxy for strategic decision-making in general, and that it serves as a perfect natural
experiment of the performance implications of cognitive biases. I end the review by
outlining the five specific hypotheses I wish to test with my dataset.
2.1 Heuristics and biases - the state of the literature
Simon (1955; 1979) argued that the rational-choice postulate had to be complemented
by a theory of bounded rationality. And later Tversky and Kahneman (1973, 1974)
established heuristics and biases as on of the pillars for future research on bounded
rationality (Kahneman, 2003). In this paper I am chiefly concerned with the
availability and representativeness heuristics. The availability heuristic is our
tendency to assess the probability of an event by how easily it can be remembered or
imagined (Tversky and Kahneman, 1974). Whilst the representativeness heuristic can
be thought of as a similarity heuristic (Thaler and Sundstein, 2008), and is our
tendency to assess the probability of an event by how similar it is to its parent
population. And our use of heuristic reasoning lead to systematic directional cognitive
biases. For instance most people incorrectly believe tornados to be a more common
cause of death than lightning, since tornados are easier to imagine and remember
(Plous, 1993). And individuals consistently rate the statement “Linda is a bank teller,
and active in the feminist movement” as more probable than the statement “Linda is a
bank teller” after reading a fictional personality sketch of Linda, depicting her as
someone representative of a feminist (Tversky and Kahneman, 1973).
The key objection to the literature on heuristics and biases have been that it is a
synthetic construct found in experiments, and that the findings does not illustrate how
people make choices in the real world (Gigerenzer, 1991; 1996; 2008). Firstly it is
argued that monetary incentives and consultation with other agents remove the biases
from our cognition (Charness et al. 2010; Charness and Sutter, 2012). And relatedly

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Wright and Goodwin (2002) argue that once people are motivated to think harder
about their choices, the biases disappear. Secondly it has been argued that real-life
strategic decisions are not analogous to the fixed questions and response-options
individuals face in the laboratory (Macrimmon and Webrun, 1986; March and
Shapira, 1987), and that strategic decision-makers are rather concerned with optimal
scenario planning than evaluating probabilities (Wright and Goodwin, 1999).
However a large body of evidence illustrating the real-world impact of cognitive
biases has gradually superseded these objections (Thaler and Sundstein, 2008).
Money and greater incentives have been demonstrated to have an inverse U-shape
effect on performance on cognitive tasks (Ariely et al. 2009). There is evidence that
individuals make systematic errors in important financial decisions, like choice of
investments (Barber and Odean, 2000; Bernatzi and Thaler, 2001) or decisions in the
real-estate market (Genesove and Mayer, 2001). Other overwhelming evidence can be
found in Thaler and Sundstein´s (2008) book Nudge, in Ariely´s books on irrationality
(2009; 2011) and in several online journals.
Recent research also points to individual differences in the degree to which we are
prone to cognitive biases. Stumpf and Haley (1989) have illustrated that the four
Jungian personality types suffer from different cognitive biases. And similarly
Busenitz and Barney (1997) have demonstrated that entrepreneurs use heuristic
reasoning more extensively than managers in large organizations. Consequently
biases such as ignorance of base-rates and overconfidence in their own chances of
success is what often leads entrepreneurs to start new firms despite their low odds of
success (Busenitz, 1999; Coelho et al. 2004; Coelho, 2010).
As a consequence of the evidence supporting the impact cognitive biases have on our
decision-making, several authors have advocated that decision-makers should develop
strategies to overcome their cognitive biases (Hammond, 1998; Bazerman and Moore,
2008). Russo and Schoemaker (1990) for instance compare becoming a good
decision-maker to becoming a good athlete. Just as top athletes recognize that
improvement depends on a training process they can systematically analyse, decision-
makers must examine and eliminate cognitive errors methodologically and
consistently.

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2.2 Empirical research on the performance implications of cognitive biases
As demonstrated cognitive biases can cloud decision-makers judgments outside the
laboratory. Yet researchers have not conclusively established how differences in
individual propensity to cognitive biases affect differences in strategic decision-
making performance (Busenitz and Barney, 1997; Fenton-O´Creevy, 2004). It is
difficult to find a good proxy for decision-making performance outside the laboratory
(Ariely, 2009; 2011). And the most widely used proxy, behavioural finance and trader
performance, has so far provided mixed results.
Grinblatt and Keloharju (2000) find that foreign institutional investors outperform
domestic investors on the Finnish stock market, by deploying momentum-trading
strategies and avoiding a home bias. Shapira & Venezia (2001) demonstrate that
individuals hold on to poorly performing stocks for longer than professional
investors2
. And similarly Dhar & Zhu (2006) find that the extrapolation bias3
is found
amongst individual Chinese investors, but not amongst institutional investors.
However studies on both mutual funds (Frazzini, 2006) and future traders (Coval and
Shumway, 2005) show that professional investors also suffer from the disposition
effect4
. Furthermore Fenton-O´Creevy et al. (2003) establish that propensity to the
illusion of control bias5
is inversely related to trader performance. But simultaneously
Chen et al. (2007) find that experienced investors are not always less prone to
behavioural biases than inexperienced investors.
Exactly why the results are so mixed is unclear, but it is possibly impacted by
somewhat different methodologies (Chen et al. 2007), focus on different biases and
cultural differences between the samples (Yates et al. 1998). Nonetheless more
empirical research should be done to clarify to what extent differences in propensity
to cognitive biases affect decision-making performance.
2 Holding onto loosing stocks can partially be traced to the cognitive biases loss-aversion,
overconfidence, and unrealistic optimism
3 A bias where investors buy past winners believing their performance to be representative of future
performance
4 The disposition effect is a cognitive bias where individuals hold on to poorly performing stock for too
long, and sell winners too early.
5 A cognitive bias where individuals erroneously believe they can control their circumstances

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2.3 Poker as a proxy for strategic decision-making
Poker is a game where individual agents compete against each other based on
probabilities, Knightian uncertainty, risk assessments and possible deception (Billings
et al. 2002). As early as in 1944 von Neumann and Morgenstern (2007) used poker as
a metaphor for economics due to its social and strategic nature. Similarly Fenton-
O´Creevy et al. (2004) find poker analogous to financial decision-making, since a
player´s success depends upon chance, his risk-return strategy and his social
judgment. Consequently poker has been classified as a game of skill, not a game of
luck (DeDonno and Detterman, 2008; Turner, 2008). And in the long-run the quality
of poker player´s choices will determine his level of success (Brunson et al. 1984;
Billings et al. 2002).
Similarly poker has been compared to the strategic dilemmas individuals face
elsewhere in life and business (Friedman, 1971; Osborne, 2004; Sklansky, 2009).
Managers for instance often face limited information, severe time-pressure,
conflicting short-term and long-term interests, and a combination of subjective and
objective data when they make strategic decisions. Not unlike what poker players do
on a regular basis (Brunson and Addington, 2002). Relatedly it has been argued in
popular press that managers should learn from game-theory (Dixit, 1993; Aschstatter,
1996; Smith, 1996), which is a key skill for successful poker players (Chen and
Ankenman, 2006; Bloch et al. 2007).
Consequently poker is a good natural experiment for evaluating strategic decision-
making. And the game also holds up to the ecological validity arguments initially
raised against laboratory testing of heuristics and biases. Poker is played with high
financial incentives6
, and people think hard about their decisions7
. And just as in
business, scenario planning, or the creation of strategies that perform well in different
situations, is key to success in poker (Siler, 2010). Additionally just as managers rely
on certain heuristics in areas such as negotiations (Thompson 2010), poker players
often adapt heuristics like “never play for an inside straight” (Bazerman and Moore,
2008).
6 The average player in my dataset has played for more than $30k online
7 As demonstrated by more than 800 books on poker strategy at Amazon.com, and countless websites
devoted to poker strategy (twoplustwo.com, donkr.com etc.)

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2.4 Hypotheses
As we have seen the theoretical dogma is that cognitive biases affect our decision-
making performance. And in the literature both the availability and representativeness
bias are mentioned as cognitive traps that should be avoided (Bazerman and Moore,
2008). As poker performance is contingent on good decisions, I expect that both
biases will impair poker performance. Thus the two first hypotheses I wish to test are:
H1: Individual propensity to the availability bias is inversely correlated with poker
performance
H2: Individual propensity to representativeness biases is inversely correlated with
poker performance
Additionally it is interesting to see how the two biases are correlated since existing
evidence is inconclusive (Dhar and Zhu, 2006). But as propensity to one bias is
probably not 100% correlated to propensity another bias (Goetzman and Kumar,
2008), there is reason to believe that the correlation between performance and biases
will be strengthened by grouping both biases together as one measure. Hence my third
hypothesis is:
H3: The combined individual propensity to both biases is more strongly correlated
with poker ability than both representativeness and availability on its own
Relatedly, due to either self-selection (Kahneman, 2003; 2011) or learning (Wolosin,
et al. 1973; Maciejovsky et al. 2013), I expect that professional decision-makers are
less prone to cognitive biases than amateurs. And my fourth hypothesis is:
H4: Professional poker players are on average less prone to cognitive biases than
amateurs
Finally good poker players compete against better opponents who make fewer
mistakes (Siler, 2010). Consequently cognitive biases should be a better predictor of
individual differences in poker ability amongst the professionals than amongst the
amateurs. Thus my fifth hypothesis is:
H5: Cognitive biases are a better correlated with poker ability amongst the
professionals than amongst the amateurs.

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3. Research Methods
In this section I describe how I performed my research. Firstly I describe my sample
of poker players. Secondly I illustrate how I measure poker performance. Thirdly I
demonstrate how my psychological survey measures individual propensity to the
availability and representativeness bias. Finally I outline how I technically tested the
five hypotheses discussed earlier.
3.1 Sample
394 individuals attempted my survey, but 56 were excluded due to incomplete
responses8
. Hence I have 338 poker players in my dataset. The sample was collected
by distributing my survey on two online poker forums (twoplustwo.com and
Donkr.com/no), posting it on the Warwick Poker Society Facebook page, and sending
it out to friends in the poker community. The sample is consequently skewed towards
individuals who play poker at high level9
, but the data still reveal significant
deviations in individual skills10
. In order to increase the response rate I created both
an English and a Norwegian version of the survey11
. The questionnaires were
translated to Norwegian by myself, and double-checked by three other Norwegian
LSE students. There are no significant differences between the Norwegian and the
English participants.
3.2 Measuring poker performance
To measure poker performance I use actual data on each player´s performance,
gathered by the online poker tracking website SharkScope. I use a measure called
Ability that takes into account a variety of measures on each individual: such as total
profit, return on investment (ROI) and average stake. Subsequently it ranks players on
a scale from 50-10012
(sharkscope.com; twoplustwo.com). This is better than simply
measuring poker performance by ROI or total profits, since players compete against
different opponents at different stakes. A player that loses marginally at high-stake
games is for instance probably making better poker decisions than an individual that
8 Anyone that answered less than 7 questions were excluded
9 The average player in my sample has earned close to $10k playing online poker and has an average
ROI of 11%. Comparatively the average online poker player looses money.
10 The standard deviation for total profits was 34k, and for average ROI it was 52%
11 Users of Donkr.com/no and poker friends are Norwegian, and not comfortable with the English
language
12 100 being the highest score

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wins marginally at very low-stakes games. By analogy it would be similar to arguing
that a footballer is performing better if he scores 5 goals in a season for the first-team,
than if he scores 15 for the reserves.
However the biggest online pokersite recently restricted its data access for third-party
applications like SharkScope (Pokerstars.com), so this measure is only available for
89 players13
. To test my hypothesis on all 338 players I therefore used individuals’
self-assessed poker ability (SAPA), on a scale from 1-10 (10 being the best). This
measure is highly correlated to SharkScope´s calculated ability, and the descriptive
statistics also demonstrate that the calculated biases are similarly correlated to SAPA
and SharkScope’s ability rank.
3.3 Individual propensity to the availability and representativeness bias
To measure representativeness I asked participants to answer to their best ability in 5
decision-making scenarios. Each question was gathered from the heuristics and biases
literature (Kahneman et al. 1982; Tversky and Kahneman, 1983; Kahneman, 2011).
And for each question the objectively wrong answer was associated with a particular
form of the representativeness bias. Building on Fong and Nisbett (1991) and
Busenitz and Barney (1997) I coded the answers by giving a score of -1 for each
question where the participants fell for the representativeness bias, a score of 0 if they
did not respond, and a score of 1 if they answered correctly. Thus participants were
given an aggregate score for their propensity to the representativeness bias ranging
from -5 to 5. With -5 being those individuals with the highest propensity to the
representativeness bias. For instance the respondents were asked about Linda, and
those who rated the statement “Linda is a bank teller and active in the feminist
movement” as more likely than the statement “Linda is a bank teller” were given a
score of -1 on that particular question. A similar approach was used to measure the
availability bias, but here participants were asked 6 questions, and the wrong answers
were associated with the availability bias. For instance participants were asked about
the most common causes of death in the US (Plous, 1993) – depicting two alternatives
against each other. For a complete overview of the questions see the appendix.
13 The number of players I have SharkScope data on is actually slightly higher, but I filtered it
based on players preferred poker game. In particular SharkScope does not track cash-game
results, so including cash-game specialists in the sample would dilute the ability measure’s
validity

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3.4 Methodology used to test the hypotheses
To test H1 and H2 I correlated the individual measure of each bias with individual
SAPA for all 338-poker players, and I also correlated biases with actual ability for my
subset of 89 players. I then proceed to test the power of the correlations (Barrow,
2009). Additionally I performed a linear regression analysis to solidify the findings.
To test H3 I used the Hotelling t-test and computed the p-value of the proposition
(Steiger, 1980).
In order to test H4 I separated the players into two groups based on their profession.
Those that sited poker as their main source of income are referred to as professionals,
whilst the others are referred to as amateurs. I compared the two groups using
descriptive statistics, and performed a one-tailed t-test assuming hetroscedastic
variation between the two groups (Barrow, 2009). A one-tailed test is appropriate
since there is reason to believe that professionals are less prone to biases than
amateurs, and the choice of hetroscedastic variance is based on differences in standard
deviations between the two groups. To test H5 I tested whether the correlations
between biases and online poker performance were significantly different for
professionals and amateurs (Fisher, 1970). As with H4 a one-tailed test is appropriate
(Barrow, 2009).
4. Results
I now present the results of my research. Firstly I demonstrate that individual
propensity to the both the availability and representativeness bias is significantly and
inversely correlated with poker ability. And the evidence suggests that the presence of
both biases in an individual, amplifies the negative correlation established between
poker ability and cognitive biases. Secondly I establish that professional poker players
are less prone to both biases than amateurs. And finally the data indicates that
cognitive biases are more detrimental to the performance of professionals than
amateurs.

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4.1 Individual propensity to both the availability and representativeness bias
are inversely correlated with poker performance
Table 1 presents the means, medians, standard deviations and correlation coefficients
between the variables for the sample of 338 players, using SAPA as the measure of
poker performance. Given the coding methodology the correlation between the biases
and poker ability should be positive14
. The correlation between the availability bias
and poker ability is 0.11, and is significant at a 5% level. This is illustrated by the
upward sloping trend-line in graph 1. Similarly the representativeness bias is
correlated with poker ability at 0.16, which is statistically significant at a 1% level.
This is illustrated by the upward sloping trend-line in graph 2.
To ensure that the results found in table 1 were not contaminated by the use of SAPA
rather than actual poker ability, table 2 presents the same statistics for the subset of 89
players where SharkScope data was available. As we can see the subanalysis indicates
that SAPA is highly correlated with actual poker ability. And the results are very
much the same, although the correlation between the availability bias and poker
ability is not statistically significant for the subanalysis. This is illustrated by the
somewhat flatter trend-line in graph 3.
14 Since a player with a propensity to a bias will have a negative mean score for that bias, the positive
correlation with poker ability indicates an inverse relationship between the variables.
Table 1 - Means, Median, Standard Deviations and Correlations for Self-Assessed Poker Ability
Variable Mean Median SD 1 2 3
1 Self-Assessed Poker Ability 6.37 7.00 1.49
2 Availability Bias -0.72 0.00 2.19 0.11**
3 Representativeness Bias -0.99 -1.00 2.38 0.16*** 0.08
4 Combination of Both Biases -1.72 -1.00 3.37 0.18*** 0.71*** 0.76***
* p < 0.1
** p < 0.05
*** p < 0.01 N = 338
-6
-4
-2
0
2
4
6
0 1 2 3 4 5 6 7 8 9 10
AvailabilityBias
Self-Assessed Poker Ability
Availability Bias and Self-Assessed Poker Ability
-6
-4
-2
0
2
4
6
0 1 2 3 4 5 6 7 8 9 10
RepresentavenssBias
Self-Assessed Poker Ability
Representa veness Bias and Self-Assessed Poker Ability
Graph 1 Graph 2

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Consequently both H1 and H2 are supported, but correlation does not equal causation
even though there is no theoretical reason to believe that poor poker performance
increases propensity to cognitive biases. To further examine the findings I performed
a linear regression analysis of the relationship between the biases and self-assessed
poker ability (Barrow, 2009). The slope coefficient is significant at a 10% level for
the availability bias and at a 1% level of the representativeness bias. And the adjusted
R^2 measure further confirms both the direction and the impact these two biases have
on poker performance, and thus solidifies the meaning accrued from H1 and H2.
Descriptively H3 is in line with the correlations presented in table 1. And graph 5
illustrates that the correlation between the combined measure of both biases with
Table 2 - Means, Median, Standard Deviations and Correlations for SharkScope Ability rank
Variable Mean Median SD 1 2 3 4
1 Poker Ability 68.27 66.00 14.43
2 Self-Assessed Poker Ability 6.53 7.00 1.49 0.39***
3 Availability Bias -0.73 0.00 2.26 0.05 0.10
4 Representativeness Bias -1.39 -1.00 2.28 0.19* -0.05 -0.02
5 Combination of Both Biases -2.12 -3.00 3.17 0.17 0.03 0.70*** 0.70***
* p < 0.1
** p < 0.05
*** p < 0.01 N = 89
-6
-4
-2
0
2
4
6
50 60 70 80 90 100
AvailabilityBias
Sharkscope Poker Ability
Availability Bias and Sharkscope Poker Ability
-6
-4
-2
0
2
4
6
50 60 70 80 90 100
RepresentavenessBias
Sharkscope Poker Ability
Representa veness Bias and Sharkscope Poker Ability
Regression Statistics
R 0.18439
R Square 0.034
Adjusted R Square 0.02823
Standard Error 1.46744
Total Number Of Cases 338
ANOVA
d.f. SS MS F p-level
Regression 2. 25.38895 12.69448 5.89513 0.00305
Residual 335. 721.38324 2.15338
Total 337. 746.77219
Coefficients Standard Error LCL UCL t Stat p-level H0 (10%) rejected?
Intercept 6.50996 0.0897 6.36202 6.65791 72.57888 0.E+0 Yes
Availability bias 0.06515 0.03658 0.00482 0.12548 1.78131 0.07577 Yes
Representativeness bias 0.09347 0.0337 0.03788 0.14905 2.77362 0.00585 Yes
T (10%) 1.64941
Table 3 - Linear Regression
Self-assessed poker ability = 6.5100 + 0.0652 * Availability bias + 0.0935 * Representativeness bias
LCL - Lower value of a reliable interval (LCL)
UCL - Upper value of a reliable interval (UCL)
Graph 3 Graph 4

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SAPA, is higher than the correlation for both individual biases. However the
Hotelling T-test reveals that the combination of both biases is only significantly better
correlated with SAPA versus the availability bias at a 10% level, and not significantly
better than the representativeness bias. Thus H3 is only partially supported.
4.2 Professional poker players are less prone to cognitive biases than
amateurs
Table 4 presents the differences between professionals and amateurs with regards to
their propensity to cognitive biases. As we can see in graph 6 professionals are less
prone to both the availability and the representativeness bias than amateurs. However
a t-test reveals that the difference is only statistically significant for the
representativeness bias. And consequently H4 is partially supported by the data. It is
worth noting that since the sample is skewed toward skilled poker players, even the
amateurs are not bad decision-makers15
. And the support for H4 may consequently be
greater in a normal population.
15 Amateurs’ average SAPA is 6 compared to 8 for the professionals
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
Availability Bias Representa veness
Bias
Combina on of Both
Biases
CorrelaonwithSAPA
Correla on between biases and Self-Assessed Poker
Ability
Table 4 - Differences i propensity to cognitive biases between professionals and amateurs
N = 338 Variables Professionals (N = 73) Amatuers (N =265) T-test of Averages
Average Self-Assessed Poker Ability 7.75 5.99 0.00
Average Availability Bias -0.92 -1.94 0.22
Average Representativeness Bias -0.37 -1.17 0.01
Average Combination of Biases -0.92 -1.94 0.02
Graph 5
¨55

17
4.3 Cognitive biases are more correlated to the performance of professionals
than amateurs
Table 5 and graph 7 illustrates the differences in correlations between both biases and
SAPA for professionals and amateurs. In line with H5 the biases seem to have a
bigger impact on the performance of professionals than on the performance of
amateurs. However when I test the significance of the differences between the two
groups following Fisher (1970), the differences are not significant. Thus there is some
indicative support of H5, but more research must be done to establish whether
cognitive biases are better correlated with poker ability for professionals than for
amateurs.
-1.94
-1.17
-1.94
-0.92
-0.37
-0.92
-2.50
-2.00
-1.50
-1.00
-0.50
0.00
Average Availability Bias Average Representa veness Bias Average Combina on of Biases
PropesnitytosufferfromBias
Differences in Propensity to Biases between Professionals and Amateurs
Amateurs
Professionals
Table 5 - Correlations between SAPS and Biases for professionals and amateurs
N = 338 Variables Professionals (N=73) Amatuers (N=265) Significance of difference between correlations
Availability Bias 0.19 0.09 0.16
Representativeness Bias 0.22 0.09 0.22
Combination of Both Biases 0.26 0.13 0.16
Graph 6
¨55
Graph 7

18
5. Discussion
I now present the implications of my research. Firstly I discuss its theoretical
implications, and argue that it pushes the literature forward by establishing that
cognitive biases impair strategic decision-making. I also highlight possible avenues of
future research to reinforce my conclusions. Secondly I discuss the practical
implications of my findings, and emphasize measures strategic decision-makers
should take to diminish the impact cognitive biases have on their choices.
5.1 Theoretical implications
As demonstrated in the literature review the theoretical consensus is that cognitive
biases can explain performance differentials between strategic decision-makers
(Hammond et al. 1998; Bazerman and Moore, 2008). Thus it has been somewhat
disconcerting that empirical research to support this theoretical proposition has had
mixed success (Frazzini, 2006; Chen et al. 2007 etc.). My results contribute to this
research by underscoring the negative effects cognitive biases have on our strategic
decision-making performance.
The confirmation of H1 and H2 demonstrates that cognitive biases are inversely
correlated to poker performance, or strategic decision-making performance in general.
Additionally the regression results reinforce both the direction and the impact the
availability and the representativeness bias have on poker performance. The
confirmation of H4 further demonstrates that cognitive biases impair good strategic
decision-making – as professionals on average make fewer cognitive errors than
amateurs. However this study only examines the performance implications of the
availability and representativeness bias. Hence future research should attempt to
answer whether these findings generalize for other biases as well. If other biases have
similar consequences, the partial confirmation of H3 indicates that a mapping of
cognitive biases can be a strong predictor of strategic decision-making performance.
Nonetheless the support for H3 is not strong, so future research should also address
this unexplored area of how different cognitive biases are related to each other (Chen
et al. 2007).
The results from H5 further indicate that cognitive biases are an even better
performance indicator amongst elite decision-makers. Intuitively this makes sense in

19
poker since elite individuals often compete against other elite individuals – so smaller
margins separate their choices and performance (Siler, 2010). But the results also
generalize to strategic decision-making in general. One of the best examples is how
Billy Beane improved the Oakland Athletics by overriding the ingrained heuristics
scouts used when looking for baseball talent, and replaced it by statistical analysis
(Lewis, 2004). The biases stemming from heuristic reasoning might not be crucial if
you are picking out the firm softball team, but amongst professionals details are key.
And eventually other baseball teams had to replicate Bean´s analytical approach to
remain competitive (Heskett, 2011; Snyder, 2012).
Thus my main contribution to the literature is empirical support of the assumption that
cognitive biases weaken strategic decision-making. Nonetheless there should be some
caution about generalizing these results. As illustrated in the literature review, the
performance implications of cognitive biases within behavioural finance is still
undecided (Fenton-O´Creevy et al. 2004; Chen et al. 2007). And even though I think
poker its a good proxy for strategic decision-making in general, it would be
interesting to see how my results generalize to different professions. Busenitz and
Barney (1997) for instance demonstrate that entrepreneurs use more heuristics and
biases in their decision-making than managers in large organizations. Subsequently
they argue that this separation is beneficial as managers must be rational to succeed,
whilst nonrational decision-making may actually be essential for the success of
entrepreneurs. This could for instance explain why great entrepreneurs often make
bad managers (Schell, 1991); we might need different skills for different tasks.
5.2 Practical implications
The findings highlight the importance of cognitive biases in explaining individual
performance differentials in strategic contexts. However a related and important
question is whether individuals are capable of changing the degree to which they
suffer from cognitive biases. Is it possible to become a better decision-maker by
getting rid of our cognitive biases? Tversky and Kahneman (1981) are sceptical since
many of the biases occur due to unconscious use of heuristics in our thinking. But if
cognitive biases are difficult to change, this implies that they may be a source of
sustained competitive disadvantage (advantage) in strategic decision-making (Barney,

20
2001), since they lead individuals to make choices in profoundly different ways
(Stumpf and Dunbar, 1991).
However many authors also argue that cognitive biases can be learned and corrected
by training (Wolosin et al. 1973; Russo and Schoemaker, 1990; Fong and Nisbett,
1991). It is not the key aim of this study to analyze this proposition, but I do believe
the confirmation of H4 provides some support for this thesis. It might be that
professional poker players are less prone to cognitive biases than amateurs due to self-
selection. However poker performance have been demonstrated to improve with
training (DeDonno and Detterman, 2008). Thus there is some reason to believe that
training can decrease propensity to cognitive biases. Intuitively it makes sense that at
least some individuals become more aware of their biases with time, and adjust their
decisions accordingly. But it would be interesting to see the results of a longitudinal,
rather than a cross-sectional, study where individuals are trained to detect and combat
their cognitive biases.
Nevertheless the practical implications of my results are that individuals should
develop strategies to overcome cognitive biases if they want to become better
decision-makers. One way of doing this is to optimize the situation you make
important decisions in. For instance cognitive biases are more likely to occur under
time-pressure (Finucane et al. 2000), if you are engaged in several mental activities
simultaneously (Gilbert, 1991), if you make decisions without consultation or debate
with others (Kahneman, 2011; Charness and Sutter, 2012) or if you are in a
particularly good mood (Isen et al. 1988; Bless et al. 1996). Groups for instance
behave more strategically and are less prone to cognitive biases than individuals
(Cooper and Kayel, 2005; Blinder and Morgan, 2005), and research indicates that
individuals take their improved knowledge with them in future endeavours
(Maciejovsky et al. 2013). Hence a poker player might decrease his susceptibility to
cognitive biases by engaging in game analysis with fellow players. Similarly firms
with dispersed power will probably commit fewer strategic errors stemming from
cognitive biases than firms with an “all-mighty CEO”.
Another way to reduce the impact of biases is by exposing yourself to statistical
thinking (Nisbett et al. 1983; Agnoli and Krantz, 1989; Shafir and LeBoeuf, 2002), or

21
by simply increasing your awareness of these biases, when they usually occur and in
what way they tend to influence you (Russo and Schoemaker, 1990; Hammond et al.
1998; Bazerman and Moore, 2008). A poker player might for instance become better
at “reading opponents’”16
if he adjusts his subjective impressions towards the mean
style of play. And a CEO contemplating launching a new product might make a better
decision, if she realizes that her assessment of the likelihood of success is probably
coloured by her recollection of the success or failures of similar products in the past
(Bazerman and Moore, 2008).
6. Conclusion and limitations
One of the key limitations of the study is that the relationship between biases and
poker performance may be clouded by other factors impacting both measures.
Specifically individuals that find thinking fun (Shafir and LeBoeuf, 2002) and have
had exposure to statistical thinking (Nisbett et al. 1983; Agnoli and Krantz, 1989;
Shafir and LeBoeuf, 2002) are less prone to cognitive biases. And to some extent
these are all traits that would also be beneficial for poker players. However recent
research demonstrates that propensity to cognitive biases is not correlated to scores on
college entrance exams (Stanovich and West, 2002), so the likelihood that cognitive
biases works as a proxy for statistical thinking or thinking ability might not be that
high.
Consequently this paper contributes to the literature by empirically demonstrating that
the availability bias and the representativeness bias are inversely correlated to poker
performance. As I have argued poker is in many veins similar to the strategic
decisions we face in business or sports, and consequently my results have practical
implications for how we should approach strategic decisions in general. To become
better decision-makers we must avoid cognitive biases, and we can do this optimizing
our decision-making environment and by training our awareness. Theoretically the
literature would benefit from further research into how these results generalize for
other biases, and for different professions. But for now it appears that cognitive biases
are amongst the details that separate good from great decision-makers.
16 Poker-slang for analysing the style of play of an opponent

22
7. Appendix
In this part I present the questions I asked participants in order to test their propensity
to the availability and the representativeness bias. They are, for the convenience of
readers, categorized by which bias they were meant to test. Following each question I
have added a short description of how the question tests each bias. For all questions
the answer associated with the respective bias is also statistically/objectively the
wrong answer. I should note that for the actual survey, participants were also asked a
few questions on their risk-profile, but the nature of the questions did not fit the
hypotheses for this paper.
7.1 The availability bias
1. Which cause of death is most common in the United States?
a) Diabetes, b) Homicide
In this question 15% answered homicide, which was the answer associated with the
availability bias. The question was adopted from Plous (1993:121), and in fact
diabetes is the much more common cause of death. It tests the ease of retrievability,
which is a particular form of the availability bias.
2. 1. Which cause of death is most common in the United States?
a) Lightning, b) Tornado
In this question 84% answered Tornado, which was the answer associated with the
availability bias. The question was adopted from Plous (1993:121), and in fact
lightning is a much more common cause of death than tornados. It tests the ease of
retrievability, which is a particular form of the availability bias.
3. Which cause of death is most common in the United States?
a) Shark Attack, b) Falling Airplane parts
In this question 50% answered Shark Attack, which was the answer associated with
the availability bias. The question was adopted from Plous (1993:121), and falling
airplane parts is a much more common cause of deaths than shark attacks. (Shark
attacks that lead to death are extremely rare…). It tests the ease of retrievability,

23
4. Without looking back at the celebrities that attended the fundraiser in London.
Please give your best indication of the number male and female celebrity participants
at the event.
At the beginning of the survey participants were given the following instructions. “At
some point in the survey you will be asked some questions about the following
participants at a recent London fundraiser. Please pay attention to the attached list of
celebrity attendees: Angelina Jolie, Brad Pitt, Brian Cox, Cameron Diaz, Emma
Watson, Jeremy Hunt, Jim Broadbent, Kate Middleton, Keira Knightley, Karl
Simmons, Kim Kardashian, Meryl Streep, Noel Clark, Prince William, Rihanna,
Rufus Sewell, Stephen Fry.”
In this question people were expected to indicate more female than male participants,
despite there actually being 8 women and 9 men on the guestlist. This was due to the
fact that the women on the list are far more famous then the men. 55% indicated that
more women than men attended the fundraiser. This question was adopted, and
modernized, from Tversky and Kahneman (1973). It tests the ease of retrievability,
5. In which of the two structures are there more paths?
a) Structure A, b) Structure B, c) Equally many paths
In this exercise 76% answered A, which was the answer associated with the
availability bias. (The correct answer is equally many paths). This question was
adopted from Tversky and Kahneman (1973). It tests the ease of imaginability, which
is a particular form of the availability bias.

24
6. Do you think there are more paths containing six X´s and no O, or more paths
containing five X´s and one O?
a) Five X´s and one O, b) Six X´s and no O
In this exercise 49% answered B, the answer associated with the availability bias.
This question was adopted from Tversky and Kahneman (1973). It tests the ease of
imaginability, which is a particular form of the availability bias.
7.2 The representativeness bias
1. Linda is thirty-one years old, single, outspoken, and very bright. She majored
in philosophy. As a student, she was deeply concerned with issues of
discrimination and social justice, and also participated in antinuclear
demonstrations. Please rank the following statements by their probability,
using 1 for the most probable and for the least probable.
A) Linda is a teacher in elementary school
B) Linda is a bank teller
C) Linda works in a bookstore and takes yoga classes
D) Linda is an insurance salesperson
D) Linda is a bank teller and is active in the feminist movement
In this question 64% rated D as more likely than B, and thus fell for the
representativeness bias. The question is adopted from Kahneman et al. (1982),
and tests a particular form of the representativeness bias called the conjunction
fallacy. This fallacy occurs since D is viewed as more representative of Linda
than B, but statistically it is impossible since D is a conjunction of B.

25
2. Bill is 34 years old. He is intelligent, but unimaginative, compulsive, and
generally lifeless. In school, he was strong in mathematics, but weak in social
studies and humanities. Please rank the following statements by their
probability, using 1 for the most probable and 5 for the least probable
A) Bill is a physician who plays poker for a hobby
B) Bill is an accountant
C) Bill plays Jazz for a hobby
D) Bill is a reporter
E) Bill is an accountant who plays jazz for a hobby
In this question 58% rated E as more likely than C, and thus fell for the
representativeness bias. The question is adopted from Kahneman et al. (1982),
and tests a particular form of the representativeness bias called the conjunction
fallacy. In this example Bill really sounds like an accountant, so people
erroneously rate E as more probable than C.
3. All families of six children in a city were surveyed. In 72 families the exact
order of births of boys and girls was GBGBBG. What is your estimate of the
number of families surveyed in which the exact order of births was BGBBBB?
(Please indicate the number of families)
In this question 65% answered that less than 72 families were their best
estimate, and thus fell for the representativeness bias. The question is adopted
from Kahneman et al. (1982), and tests a particular form of the
representativeness bias called the gambler´s fallacy. In this case people tend to
believe that the order BGBBBB is less probable than GBGBBG since
GBGBBG better reflects the salient features of a random process, despite both
outcomes being equally probable.

26
4. Steve is drawn from a random sample in the US. A neighbor describes him as
follows: “Steve is very shy and withdrawn, invariably helpful but with little
interest in people or the world of reality. A meek and tidy soul, he has a need
for order and structure, and a passion for detail”. Is Steve most likely to be a:
a) Farmer, b) Librarian
In this question 72% answered that Steve was more likely to be a Librarian,
and thus fell for the representativeness bias. It is adopted from Kahneman
(2011), and tests a particular form of the representativeness bias called the
base rate fallacy. In this example people find Steve to be more representative
of a librarian than a farmer, and often neglects that there are more than 20
times as many farmers as librarians in the US.
5. A certain town is served by two hospitals. In the larger hospital about 45
babies are born each day, and in the smaller hospital about 15 babies are born
each day. As you know, about 50% of babies are boys. The exact percentage
of baby boys, however, varies from day to day. Sometimes it may be higher
than 50%, sometimes lower. For a period of 1 year, each hospital recorded the
days on which more than 60% of the babies born were boys. Which hospital
do you think recorded more such days?
a) The smaller hospital, b) The larger hospital, c) About the same (i.e. within
5% of each other)
In this question 35% answered either b or c, and thus fell for the
representativeness bias. It is adopted from Kahneman et al (1982), and tests a
particular form of the representativeness bias called the “law of small
numbers”. In this example any extreme sample is much more likely to come
form a small sample, but people often ignore this and don´t find a) any more
representative than the other to answers.

27
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35
6.1 Additional Resources
Twoplustwo.com
Donkr.com/no
Pokerstars.com
Sharkscope.com

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