Your SlideShare is downloading. ×
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
The cost of irrationality - how poker players perform better by avoiding cognitive biases
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

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

1,131

Published on

The article demonstrates how cognitive biases detrimentally strategic decision-making. …

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! :)

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,131
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 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
  • 2. 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
  • 3. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 3 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
  • 4. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 4 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)
  • 5. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 5 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”…
  • 6. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 6 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
  • 7. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 7 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.
  • 8. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 8 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
  • 9. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 9 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.)
  • 10. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 10 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.
  • 11. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 11 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
  • 12. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 12 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
  • 13. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 13 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.
  • 14. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 14 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
  • 15. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 15 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
  • 16. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 16 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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, which is a particular form of the availability bias.
  • 23. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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, 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.
  • 24. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 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. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 27 7. Bibliography Agnoli, F., & Krantz, D. H. (1989). Suppressing natural heuristics by formal instruction: The case of the conjunction fallacy. Cognitive Psychology, 21(4), 515- 550. Ariely, D. (2009). Predictably irrational, revised and expanded edition: The hidden forces that shape our decisions. HarperCollins. Ariely, D. (2011). Upside of Irrationality: The Unexpected Benefits of Defying Logic at Work and at Home. HarperCollins UK. Ariely, D., Bracha, A., & Meier, S. (2009). Doing good or doing well? Image motivation and monetary incentives in behaving prosocially. The American Economic Review, 99(1), 544-555. Aschstatter (1996), Let Game Theory Begin: Anticipating Your Rival, Investor´s Business Daily Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance, 55(2), 773-806. Barney, J. B. (2001). Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of management, 27(6), 643-650. Barrow, M. (2009). Statistics for economics, accounting and business studies. Fifth Edition, Pearson Education. Bazerman, M.H. and Moore, D.A. (2008), Judgment in Managerial Decision Making, 7th ed. New York: Wiley.
  • 28. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 28 Benartzi, S., & Thaler, R. H. (2001). Naive diversification strategies in defined contribution saving plans. American economic review, 79-98. Billings, D., Davidson, A., Schaeffer, J., & Szafron, D. (2002). The challenge of poker. Artificial Intelligence, 134(1), 201-240. Bless, H., Clore, G. L., Schwarz, N., Golisano, V., Rabe, C., & Wölk, M. (1996). Mood and the use of scripts: Does a happy mood really lead to mindlessness?. Journal of personality and social psychology, 71(4), 665. Blinder, A. S., & Morgan, J. (2005). Are two heads better than one? Monetary policy by committee. Journal of Money, Credit and Banking, 789-811. Bloch, A., Brodie, R., Ferguson, C., Forrest, T., Furst, R., Gordon, P., ... & Sexton, K. (2007). The Full Tilt Poker Strategy Guide: Tournament Edition. Hachette Digital, Inc.. Brunson, D., Baldwin, B., Caro, M., Hawthorne, J., Reese, D., & Sklansky, D. (1984). Super/system: A course in power poker. B & G Pub.. Busenitz, L. W. (1999). Entrepreneurial Risk and Strategic Decision Making It’s a Matter of Perspective. The Journal of Applied Behavioral Science, 35(3), 325-340. Busenitz, L. W., & Barney, J. B. (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of business venturing, 12(1), 9-30. Charness, G., Karni, E., & Levin, D. (2010). On the conjunction fallacy in probability judgment: New experimental evidence regarding Linda. Games and Economic Behavior, 68(2), 551-556. Charnesss, G., & Sutter, M. (2012). Groups make better self-interested decisions. The Journal of Economic Perspectives, 26(3), 157-176.
  • 29. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 29 Chen, B., & Ankenman, J. (2006). The mathematics of poker. 1 edition, Conjelco Chen, G., Kim, K. A., Nofsinger, J. R., & Rui, O. M. (2007). Trading performance, disposition effect, overconfidence, representativeness bias, and experience of emerging market investors. Journal of Behavioral Decision Making, 20(4), 425-451. Coelho, M., de Meza, D., & Reyniers, D. (2004). Irrational exuberance, entrepreneurial finance and public policy. International Tax and Public Finance, 11(4), 391-417. Coelho, M. P. (2010). Unrealistic optimism: still a neglected trait. Journal of business and psychology, 25(3), 397-408. Cooper, D. J., & Kagel, J. H. (2005). Are two heads better than one? Team versus individual play in signaling games. American Economic Review, 477-509. Coval, J. D., & Shumway, T. (2005). Do behavioral biases affect prices?. The Journal of Finance, 60(1), 1-34. DeDonno, M. A., & Detterman, D. K. (2008). Poker is a skill. Gaming Law Review, 12(1), 31-36. Dhar, R., & Zhu, N. (2006). Up close and personal: Investor sophistication and the disposition effect. Management Science, 52(5), 726-740. Dixit, A. K. (1993). Thinking strategically: The competitive edge in business, politics, and everyday life. WW Norton & Company. Fenton‐ O'Creevy, M., Nicholson, N., Soane, E., & Willman, P. (2003). Trading on illusions: Unrealistic perceptions of control and trading performance. Journal of Occupational and Organizational Psychology, 76(1), 53-68. Fenton-O'Creevy, M., Nicholson, N., Soane, E., & Willman, P. (2004). Traders: Risks, decisions and management in financial markets. Oxford University Press.
  • 30. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 30 Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of behavioral decision making, 13(1), 1-17. Fisher, S. R. A. (1970). Statistical methods for research workers (Vol. 14, pp. 140- 142). Edinburgh: Oliver and Boyd. Retrieved from: http://psychclassics.yorku.ca/Fisher/Methods/chap6.htm, accessed on 25.06.2013 Fong, G. T., & Nisbett, R. E. (1991). Immediate and delayed transfer of training effects in statistical reasoning. Journal of Experimental Psychology: General, 120(1), 34. Frazzini, A. (2006). The disposition effect and underreaction to news. The Journal of Finance, 61(4), 2017-2046. Friedman, L. (1971). Optimal bluffing strategies in poker. Management Science, 17(12), B-764. Genesove, D., & Mayer, C. (2001). Loss aversion and seller behavior: Evidence from the housing market. The Quarterly Journal of Economics, 116(4), 1233-1260. Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases”. European review of social psychology, 2(1), 83-115. Gigerenzer, G. (1996), On narrow norms and vague heuristics: A reply to Kahneman and Tversky, Psychological Review, 103(3), Jul 1996, 592-596. Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3(1), 20-29. Gilbert, D. T. (1991). How mental systems believe. American psychologist, 46(2), 107-119
  • 31. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 31 Goetzmann, W. N., & Kumar, A. (2008). Equity portfolio diversification*. Review of Finance, 12(3), 433-463. Grinblatt, M., & Keloharju, M. (2000). The investment behavior and performance of various investor types: a study of Finland's unique data set. Journal of Financial Economics, 55(1), 43-67. Haley, U. C., & Stumpf, S. A. (1989). COGNITIVE TRAILS IN STRATEGIC DECISION‐ MAKING: LINKING THEORIES OF PERSONALITIES AND COGNITIONS*. Journal of Management Studies, 26(5), 477-497. Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). The hidden traps in decision making. Harvard Business Review, 76(5), 47-58. Heskett, James (2001). “How Will the “Moneyball Generation” Influence Management?”, retrieved from: http://hbswk.hbs.edu/item/6787.html , accessed on 05.08.2013 Hilton, D. J. (2001). The psychology of financial decision-making: Applications to trading, dealing, and investment analysis. The Journal of Psychology and Financial Markets, 2(1), 37-53. Isen, A. M., Nygren, T. E., & Ashby, F. G. (1988). Influence of positive affect on the subjective utility of gains and losses: it is just not worth the risk. Journal of personality and Social Psychology, 55(5), 710. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American economic review, 93(5), 1449-1475. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press. Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
  • 32. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 32 Lewis, M. (2004). Moneyball: The art of winning an unfair game. WW Norton & Company. Maciejovsky, B., Sutter, M., Budescu, D. V., & Bernau, P. (2013). Teams Make You Smarter: How Exposure to Teams Improves Individual Decisions in Probability and Reasoning Tasks. Management Science, 59(6), 1255-1270. MacCrimmon KR, Wehrung DA (1986). Taking Risks: The Management of Uncertainty. Free Press: New York. Mosier, K. L., Skitka, L. J., Heers, S., & Burdick, M. (1998). Automation bias: Decision making and performance in high-tech cockpits. The International journal of aviation psychology, 8(1), 47-63. Neale, M. A., & Bazerman, M. H. (1983). Role of Perspective-Taking Ability in Negotiating under Different Forms of Arbitration, The. Indus. & Lab. Rel. Rev., 36, 378-388 Nisbett, R. E., Krantz, D. H., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90(4), 339. Osborne, M. J. (2004). An introduction to game theory (Vol. 3, No. 3). New York: Oxford University Press. Pitz, G. F., & Sachs, N. J. (1984). Judgment and decision: Theory and application. Annual Review of Psychology, 35(1), 139-164. Plous, S. (1993). The psychology of judgment and decision making. Mcgraw-Hill Book Company. Russo, J. E., Schoemaker, P. J., & Russo, E. J. (1990). Decision traps: Ten barriers to brilliant decision-making and how to overcome them. Simon & Schuster. Schell, J. (1991). In defense of the entrepreneur. Inc. 13(5):28-30
  • 33. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 33 Shafir, E., & LeBoeuf, R. A. (2002). Rationality. Annual review of psychology, 53(1), 491-517. Shapira, Z., & Venezia, I. (2001). Patterns of behavior of professionally managed and independent investors. Journal of Banking & Finance, 25(8), 1573-1587. March, J. G., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management science, 33(11), 1404-1418. Siler, K. (2010). Social and psychological challenges of poker. Journal of Gambling Studies, 26(3), 401-420. Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 69(1), 99-118. Simon, H. A. (1979). Rational decision making in business organizations. The American economic review, 69(4), 493-513. Sklansky, D., & Miller, E. (2006). No-limit Hold’em: Theory and practice. Henderson, NV: Two Plus Two Publishing. Smith, R. W. (1996). Business as war game: A report from the battlefront. Fortune, 134(6), 190. Snyder, C. J. (2012). Moneyball Lawyering. Ark. L. Rev., 65, 837-1031. Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of personality and social psychology, 94(4), 672. Steiger, J.H. (1980), Tests for comparing elements of a correlation matrix, Psychological Bulletin, 87, 245-251 Stumpf, S. A., & Dunbar, R. L. (1991). The Effects of Personality Type on Choices Made in Strategic Decision Situations*. Decision Sciences, 22(5), 1047-1072.
  • 34. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 34 Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. Thompson, L. (2009). The Mind and Heart of the negotiator (4th Edition.), Prentice Hall Turner, N. E. (2008). Viewpoint: Poker Is an Acquired Skill. Gaming Law Review and Economics, 12(3), 229-230. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. science, 185(4157), 1124-1131. Wolosin, R. J., Sherman, S. J., & Till, A. (1973). Effects of cooperation and competition on responsibility attribution after success and failure. Journal of Experimental Social Psychology, 9(3), 220-235. Von Neumann, J., & Morgenstern, O. (2007). Theory of games and economic behavior (4th Edition). Princeton university press. Wright, G., & Goodwin, P. (1999). Future‐ focussed thinking: combining scenario planning with decision analysis. Journal of Multi‐ Criteria Decision Analysis, 8(6), 311-321. Wright, G., & Goodwin, P. (2002). Eliminating a framing bias by using simple instructions to ‘think harder’and respondents with managerial experience: comment on ‘breaking the frame’. Strategic management journal, 23(11), 1059-1067. Yates, J. F., Lee, J. W., Shinotsuka, H., Patalano, A. L., & Sieck, W. (1998). Cross- cultural variations in probability judgment accuracy: Beyond general knowledge overconfidence?. Organizational Behavior and Human Decision Processes, 74, 89- 117.
  • 35. Candidate number: 74406 Title: Cognitive biases – the difference between good and great decision-makers 35 6.1 Additional Resources Twoplustwo.com Donkr.com/no Pokerstars.com Sharkscope.com

×