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Candidate number: 74406 Title: Cognitive biases the difference between good and great decision-makers 1 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

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

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The article demonstrates how cognitive biases detrimentally strategic decision-making. In particular - it illustrates how poker players perform better by avoiding the availability and representativeness bias. Finally it illustrates some advice on how to avoid these cognitive errors and improve your decision-making! :)

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Page 1: 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

1

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

Page 2: 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

2

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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Candidate number: 74406 Title: Cognitive biases – the difference between good and

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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 bias

3 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 decisions

7. 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.

Table1-Means,Median,StandardDeviationsandCorrelationsforSelf-AssessedPokerAbilityVariable Mean Median SD 1 2 3

1 Self-AssessedPokerAbility 6.37 7.00 1.49

2 AvailabilityBias -0.72 0.00 2.19 0.11**3 RepresentativenessBias -0.99 -1.00 2.38 0.16*** 0.08

4 CombinationofBothBiases -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-AssessedPokerAbility

AvailabilityBiasandSelf-AssessedPokerAbility

-6

-4

-2

0

2

4

6

0 1 2 3 4 5 6 7 8 9 10

Representa

venssBias

Self-AssessedPokerAbility

Representa venessBiasandSelf-AssessedPokerAbility

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

Table2-Means,Median,StandardDeviationsandCorrelationsforSharkScopeAbilityrankVariable Mean Median SD 1 2 3 4

1 PokerAbility 68.27 66.00 14.432 Self-AssessedPokerAbility 6.53 7.00 1.49 0.39***

3 AvailabilityBias -0.73 0.00 2.26 0.05 0.104 RepresentativenessBias -1.39 -1.00 2.28 0.19* -0.05 -0.025 CombinationofBothBiases -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

SharkscopePokerAbility

AvailabilityBiasandSharkscopePokerAbility

-6

-4

-2

0

2

4

6

50 60 70 80 90 100

Representa

venessBias

SharkscopePokerAbility

Representa venessBiasandSharkscopePokerAbility

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

AvailabilityBias Representa venessBias

Combina onofBothBiases

Correla

onwithSAPA

Correla onbetweenbiasesandSelf-AssessedPokerAbility

Table4-Differencesipropensitytocognitivebiasesbetweenprofessionalsandamateurs

N=338 Variables Professionals(N=73) Amatuers(N=265) T-testofAverages

AverageSelf-AssessedPokerAbility 7.75 5.99 0.00

AverageAvailabilityBias -0.92 -1.94 0.22

AverageRepresentativenessBias -0.37 -1.17 0.01

AverageCombinationofBiases -0.92 -1.94 0.02

Graph 5

¨55

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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

0.00AverageAvailabilityBias AverageRepresenta venessBias AverageCombina onofBiases

PropesnitytosufferfromBias

DifferencesinPropensitytoBiasesbetweenProfessionalsandAmateurs

Amateurs

Professionals

Table5-CorrelationsbetweenSAPSandBiasesforprofessionalsandamateursN=338 Variables Professionals(N=73) Amatuers(N=265) Significanceofdifferencebetweencorrelations

AvailabilityBias 0.19 0.09 0.16

RepresentativenessBias 0.22 0.09 0.22

CombinationofBothBiases 0.26 0.13 0.16

Graph 6

¨55

Graph 7

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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

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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,

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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

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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

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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,

which is a particular form of the availability bias.

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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,

which is a particular form of the availability bias.

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.

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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.

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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.

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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.

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6.1 Additional Resources

Twoplustwo.com

Donkr.com/no

Pokerstars.com

Sharkscope.com