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Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong potential as a model system for studying high-stakes, high-risk expert performance. Poker has been increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In this review, we discuss how these studies have begun to inform us about the interaction between emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion regulation; poker thus offers a good environment for studying these skills in controlled experimental settings of high external validity. We conclude by suggesting ideas for future research on expertise, with new insights provided by poker.
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https://www.journalofexpertise.org
Journal of Expertise / June 2020 / vol. 3, no. 2
Poker as a Domain of Expertise
Jussi Palomäki1, Michael Laakasuo1, Benjamin Ultan Cowley1,2, and Otto Lappi1
1Cognitive Science, Department of Digital Humanities, University of Helsinki,
Finland
2 Faculty of Educational Sciences, University of Helsinki, Finland
Correspondence: jussi.palomaki@helsinki.fi
Abstract
Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a
complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong
potential as a model system for studying high-stakes, high-risk expert performance. Poker has been
increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In
this review, we discuss how these studies have begun to inform us about the interaction between
emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in
poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion
regulation; poker thus offers a good environment for studying these skills in controlled experimental
settings of high external validity. We conclude by suggesting ideas for future research on expertise, with
new insights provided by poker.
Keywords
Economic decisions, probabilistic decision-making, risk, expertise, poker
Introduction
In everyday and expert settings, humans are able
to cope with high levels of complexity and
ambiguity. We are able to make economic
decisions under time pressure, on the basis of
limited information, and with various levels of
risk and uncertainty associated with the
outcomes. Most of the decisions are menial,
such as which type of bread to buy for dinner;
others are personally and professionally
significant, such as whether to trade a stock at a
given price. Some decisions may even be life
changing, such as deciding to undergo surgery
on short notice. How humans make such
decisions is a foundational issue in behavioral
economics, and in social and cognitive
psychology. This issue is also important for
research on expertise, because some decisions
(such as trading stocks) are made in a manner
that may be conducive to the development of
expertise (involving repeated performance,
explicit criteria for decision quality, competitive
environment, and feedback).
Ultimately, to understand expertise in risky
decision-making we need to discover what
psychological mechanisms underpin both the
success and failure of decisions in complex,
ambiguous, and intricate real-world settings
(Klein, 2008; 2015). Unfortunately, the settings
of such real-world problems are generally not
readily amenable to traditional experimental
methods. Therefore, the cognitive underpinnings
of human decisions are often investigated in
highly simplified laboratory tasks, which are
intended to capture some hypothetical
mechanism or essential aspect of real-world
problems (Buelow & Blaine, 2015; Buelow &
Suhr, 2009; Kahneman & Tversky, 1979). This
Journal of Expertise
2020. Vol. 3(2)
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under the terms of the
Creative Commons
Attribution 3.0 License.
ISSN 2573-2773
Palomäki et al. (2020) Poker as Domain of Expertise
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Journal of Expertise / March 2020 / vol. 3, no.2
creates a tension: Restricted tasks abstract away
much of real-world domain complexity,
ambiguity, and the “world knowledge” that
experts1 bring to bear on the task. This allows
one to arbitrate more definitively among
competing mechanistic hypotheses, but also
raises the question of whether those putative
mechanisms are a factor in more realistic
settings.
Laboratory tasks are meant to be analogues
of real-world environments, but whether the
tasks actually have relevance outside the
laboratory has to be taken on faith (that is,
researchers’ intuition for how similar their
simplified, abstract decision-making task really
is to a real-world task setting). Traditional
decision-making tasks are thus designed for
laboratory convenienceoften presented in text
or numerical form using novice subjects and/or
with domain-general problems. This makes
them particularly limited in terms of shedding
light on skilled decision-making processes in
rich and more natural contexts.
The study of games has been a valuable
route for cognitive scientists and can offer some
middle ground between experimental control
and ecological validity. Most everyday natural
decisionssuch as choosing ingredients for
cooking a meal or deciding on what to wear to a
partycannot be given comprehensive
mathematical definitions, nor are there often
clear normative criteria on the goodness of a
decision. However, many games are everyday
tasks with definable rules that can be compactly
represented. Also, gameplay offers means to
design recurring situations that can be used to
present decision-making tasks that have both
experimental control and high ecological
validity (such as choosing the next move in
chess). Game decisions can, moreover, often be
varied in terms of task difficulty and complexity
to suit particular participants or experimental
questions. Finally, mathematical analysis of
games has in many cases provided normative
standards whereby decision quality is evaluated.
In this review we show how these desirable
characteristics apply to the game of poker,
which can serve as a valuable model system for
studying expert economic decision-making
under risk and uncertainty. Poker is a well-
structured game played in a social setting with
many different game variants involving
randomness and probabilistic economic
decision-making. These aspects of poker make
it attractive for various scientific disciplines
interested in economic or rational decision-
making at the individual and social levels. Poker
also comes with a very large online community
of players generating big datasets and powerful
data-gathering opportunities (e.g., Eil & Lien,
2014; Siler, 2010) similar to many electronic
sports (Esports) games (e.g., Thompson,
McColeman, Stepanova, & Blair, 2017). In
general, games that have gone online provide
enormous research opportunities—poker in
particular, given its long history of defined
analytic structure, game theoretical analysis, as
well as large player base.
We will argue that poker also offers a novel
look into expertise, since the concept of poker
skill is more complex than the much-studied
technical skill in other well-studied game
domains such as chess. This is due to the
element of chance in the game: Skilled poker
players need to have emotional tolerance of
outcome variabilitythat is, to be successful
they need to able to control and reflect on their
negative emotions when even right choices can
lead to catastrophically bad outcomes merely
due to chance. Compared with chess, poker is
also typically played with a larger group of
people, making emotion regulation particularly
important.
So far, this element of poker has not been
thoroughly studied, despite the potentially
significant benefits for decision- and cognitive
sciences. Therefore, poker has strong, but as yet
untapped, potential for research on social- and
cognitive psychology, decision-making, and
expert performance.
Overall, while poker has received a lot of
attention outside academia2, up until recently
much of the research on poker has been
clinically motivated; for example, evaluating
how pathological gambling behavior manifests
in poker, or theoretically focused on using poker
as a testbed for artificial intelligence (see Brown
& Sandholm, 2019; Moreau, Chabrol, &
Palomäki et al. (2020) Poker as Domain of Expertise
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Chauchard, 2016; and Rubin & Watson, 2011).
We argue that games of economic decisions
such as poker can and should be used more in
basic behavioral research on decision-making
and expertise under risk and uncertainty. The
element of randomness may make mastering
poker different compared to mastering many
deterministic gameshowever, as articulated
by Siler (2011), it is precisely this stochastic
nature of poker that makes it a much more
realistic task environment reflecting the vagaries
and uncertainties of many real-life phenomena
such as financial decisions.
In our review we first provide a section
describing the technical aspects of poker and
explain the basic structures of the game and how
the element of chance influences skill
development. Then we address the following
review research questions (RQs): (1) What are
the components of poker skill? We describe how
the concept of poker skill comprises both
technical (mathematical, statistical and game
theoretical) and emotion regulation (“mental
game”) components, and how various social
elements of the game can bias players’ decision-
making. (2) How do poker skills develop into
expertise, and how does poker allow study of
expertise? We link the components and
development of poker skill to previous work on
expertise, deliberate practice, and skilled
intuition and show that poker offers a novel way
to look at expertise and expert performance due
to its emotion regulatory skill aspects. (3) How
can future studies on expertise and decision-
making make use of poker? We conclude our
review by detailing how future studies can draw
insights from poker to examine skilled decision-
making under emotional and social constraints.
Table 1 illustrates the features of poker
reviewed in this paper and their relevance to
research on expertise and decision-making.
Basic Properties of Poker
In every poker variant the winnings of one
player are the losses of another (poker is a zero-
sum game; Wright, 2001). Decisions in poker
are economic decisions made in partially
unpredictable environments with potentially
undesirable outcomes (it is a game of
randomness and risk). Players have to decide
between various options and act without seeing
the other players’ cards (it is a game of
incomplete information (Sklansky & Malmuth,
1999). Players must also adapt to changes in the
nature of game information across the phases of
the game, and, according to Salen and
Zimmerman (2004, p. 149) poker contains
several types of information. Furthermore, while
the game is turn based, the pace of game play
between human opponents still often creates
substantial time pressure3. The time used for
deliberation can also indirectly disclose
Table 1: Features of poker and their relevance for decision-making and expertise researchers
Poker Feature
Research Relevance; Poker allows study of the following:
Incomplete information
Microcosm of naturalistic financial decision-making
Interplay of skill and chance
Skill perception and biases in decision-making: in the short
term, bad players may win (inflated skill perception), and good
players may lose (obfuscation of true skill)
Male-dominated social environment
Masculinity and gender stereotypes in a competitive setting,
gender biased decision-making
Technical and emotional aspects of skill
Interplay between emotion regulation ability and decision-
making accuracy
Multiple sources of both public and private
information
“Game theory optimization” strategies, how skilled players
avoid exploitation
Skilled intuition
Ecologically valid skilled intuition in a “medium validity” (as
opposed to “high validity”; e.g., chess) environment
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information on one’s strategy, further pressuring
players to control their behavior. Poker is also a
dynamic environment, as the “game state”
changes even when the agent does nothing. The
social pressure and the monetary stakes
involved create additional cognitive and
emotional loadfor professional players the
rewards can reach millions of US dollars.
In the technical examples that follow, we
will focus on the most popular variant of poker
called No Limit Texas Hold’Em (NLHE). In
NLHE, each player is first dealt two cards, and
the goal is to form the best five-card
combination from one’s own two cards (not
seen by the other players) plus cards dealt on the
table (shared with all other players). There are
up to four rounds of betting, during which the
number of publicly shared cards increases,
starting with no shared cards and ending with, at
most, five. Between each round the players can
make investment decisions on whether to keep
playing, how much to invest in the pot, or give
up (i.e., fold)4. The pot will go to the winner (or
split between winners in case of ties), who is the
player with the best card combination, or the
only one not to have folded.
Skill and Chance
Generally, poker is viewed as a game of both skill
and chance, but the extent to which one or the
other dominates is debated (Croson, Fishman, &
Pope, 2008; Dedonno & Detterman, 2008; Fiedler
& Rock, 2009; Levitt & Miles, 2014; Meyer, von
Meduna, & Brosowski, 2013). Anecdotal
evidence supports the view of poker as a game
where one’s skills can constantly be improved
(Brunson, 2005; Sklansky & Malmuth, 1999;
Tendler, 2011). The consensus view in academic
discussion is that although chance plays a role in
short-term results, with enough skill poker can be
played profitably in the long run. Empirical
support for this view comes from an analysis of
456 million online poker hands (van Loon, van
den Assem, & van Dolder, 2015). Van Loon et al.
(2015) created a simulation based on these data,
comparing the best players with the worst ones,
and found that skill starts to dominate chance
when performance is assessed over about 1,500 or
more hands of play (see Fiedler & Rock, 2009, for
similar results). Skill has a demonstrably
significant role also in real-world poker success.
Professional players are consistently more
successful than amateurs at the World Series of
Poker (Croson et al., 2008; Levitt & Miles, 2014).
One way to illustrate the role of chance in
poker is through simulations of outcome
variability. Players’ levels of skill are reflected in
their win rate, which is the average amount of
profit over some number of played hands (usually
100; van Loon et al., 2015). The standard
deviation of a player’s win rate (a measure of
outcome variability) can be 20 times higher than
the win rate itself (Billingham et al., 2013). To
illustrate, we will compare two equally skilled
hypothetical players playing 200,000 hands each.
By assuming both players have somewhat low
win rates (on the statistical edge of making long-
term profit), we might observe the situation
presented in Figure 1: One player could be
winning substantially (> 15 000 €), and the other
clearly losing (-5000 €). Outcome variability is
thus a highly significant factor, masking a player’s
“true” skill as defined by the expected long-run
winnings (dashed line in Figure 1). This means
that while poker differs from games of pure
chance (such as roulette) or games of skill and
chance where long-term profit is unattainable
(e.g., blackjack; Bjerg, 2010), outcome variability
still makes it challenging to empirically estimate
the actual skill level of any individual player from
naturalistic play data5.
However, player skill can also be estimated
experimentally, by using representative decision-
making tasks, with known normative solutions:
more (technically) skilled players should
consistently reach that solution more quickly
and/or reliably. In two laboratory studies (Linnet
et al., 2010; 2012), those who had played poker at
least once a week for at least a year were better at
estimating betting outcomes than less experienced
ones. Two online studies with simplified poker
tasks showed that the amount of poker experience
was strongly and positively associated with
making mathematically appropriate poker
decisions (Laakasuo, Palomäki, & Salmela, 2015;
Palomäki, Laakasuo, & Salmela, 2013a). Thus,
components of poker skill can be isolated and
studied both “in the wild” and in the laboratory.
Palomäki et al. (2020) Poker as Domain of Expertise
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Figure 1. A simulation of 900 NLHE “poker playerswith equal win rates. Win rates are based
on “big blinds”’; that is, the minimum bet size allowed by the rules. These win rates are
calculated based on 3 big blindsin this case, eurosper 100 hands played, with a standard
deviation (SD) of 80 for the win rates (typical win rate SDs in NLHE are 70-90; Billingham et
al., 2013). Note that the “players” are simulated processes based on two parameters (win rate and
SDwin rate) and thus independent of one another. The figure depicts only the highest and lowest
earning players (top and bottom curve, respectively), and the expected value of earnings for all
900 players (dashed line). (Translated into English from Laakasuo, Palomäki, & Lappi, 2015).
Components of Poker Skill (RQ1)
In this section we address our first research
question on the components of poker skill. We
consider what is required of a good poker
player; that is, someone who is generally able to
make a long-term profit by playing poker. We
propose a division of poker skill into technical
and emotion regulatory (sub)skills.
Technical poker skills refer to in-depth
knowledge of game mechanics and betting
strategies, and how to apply them to increase
one’s chances of winning. In poker, technical
skills alone are not enough for long-term
success if dysfunctional emotional responses
systematically impair players’ decision-making.
Ample evidence shows that emotions have a
significant impact on success in poker, and
emotion regulation skills are necessary to play
poker consistently at a high level. Below, we
explain how acquiring mastery of poker
involves not only technical and strategic
knowledge of the game but also an aspect of
“mind management” or mental game ability.
Technical Skills
In terms of technical skill elements, Billings and
colleagues (2002) have proposed that in order to
play poker, one needs to understand at least the
following concepts: (1) hand strength and hand
potential, (2) betting strategy, bluffing,
unpredictability, and (3) opponent modeling.
Palomäki et al. (2013a), among others, have
suggested that (4) bankroll management is also
vitally important. These four key elements are
explained below.
Hand strength and hand potential refer to
how strong a player’s hand currently is and the
probability of a given hand strength changing
Palomäki et al. (2020) Poker as Domain of Expertise
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relative to the opponents’ assumed hand
strengthsas further cards are dealt (see the
Appendix for detailed examples). Calculations
of hand strength and hand potential require
knowledge of poker betting odds in a given
situation, mathematical aptitude, and working
memory capacity (e.g., DeDonno, 2016; Meinz
et al., 2012).
Betting strategy, bluffing, and unpredictability
refer to knowledge of when and how much to bet
or raise (or fold) in a sufficiently unpredictable
manner to maximize one’s profit and protect
oneself from exploitation. Betting strategy refers
for instance to the decision to bluff with a fixed
frequency or not, as a player might decide a
priori to bluff a given number of times in a
game. These skill elements require players to
apply (either explicitly or implicitly) the
concepts of game theory, such as Nash
equilibrium6, in their own decision-making.
Opponent modeling refers to estimating the
full range of an opponent’s possible hands.
Specifically, opponent modeling relates to how
various behavioral and social opponent
characteristics, such as betting patterns, physical
“tells”, or gender, influence the way (or what
range of possible hands) one’s opponents are
predicted to playand, consequently, how they
should be played against to maximize profit.
This generally requires interpreting concealed
social signals, reading covert facial expressions,
and detecting deception in general.
Bankroll management is the knowledge of
how much money is needed for playing, in
relation to the stakes played, to avoid going
broke. That is, how much capital is needed to
withstand outcome variance and avoid “going
broke due to merely bad luck.” Good bankroll
management skills are typically associated with
a good understanding of the concepts of
statistical variance and risk of ruin (Browne,
1989; Palomäki et al., 2013a).
The depth of the technical aspects of poker
is evidenced by clear differences in technical
skill between proficient and novice poker
players. For example, in a laboratory experiment
St. Germain and Tenenbaum (2011) compared
the performance of proficient players, with
significant tournament success, to intermediate
and novice poker players in a simulated poker
task during which participants had to “think out
loud.” Proficient players outperformed both
intermediate and novice players (in terms of
profit), and self-reported the highest amount of
thought processing and attention to relevant
technical aspects of the tasksuch as betting
patterns, estimated opponent ability, future
opponent actions and “tells,” and hand selection
and strength. Practicing these skill elements
leads to better performance: DeDonno and
Detterman (2008) conducted a laboratory
experiment where naïve poker players
systematically practiced technical poker
concepts which lead to improved success in the
game. The players were given information and
feedback about (1) when and why to pay
attention to the other players’ decisions; (2) the
concept of playing fewer hands, and how to play
them; and (3) hand ranking strategy with quality
values for the initial hand. These correspond to
opponent modeling, betting strategy, and
evaluating hand strength and hand potential,
respectively.
Poker is a knowledge-rich domain, with
complex demands on both technical and
strategic skills. These demands present
information processing challenges requiring the
player to go beyond the information embodied
in the cards and explicit in the rules. We have
provided a detailed poker task analysis in the
Appendix, which illustrates the complexities
involved in poker decision-making.
Poker is also well-suited to facilitate study
of players’ information processing because the
relationship between different forms of
information is relatively clear and understood.
The above aspects of technical poker skill
embody different challenges of information
manipulation (Salen & Zimmerman, 2004, p.
148), in the sense of the information embodied
by the cards as defined by the rules of poker.
This information can take multiple forms,
including: (1) Information known to all players;
i.e., the five “community cards” shown on the
table; (2) Information known to only one player;
i.e. the two “hole” cards of each player; (3)
Information known to the game only; e.g., the
unused cards in the deck; and (4) Randomly
Palomäki et al. (2020) Poker as Domain of Expertise
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Journal of Expertise / March 2020 / vol. 3, no.2
generated information; i.e., the shuffling of the
deck. Part of the technical skill in poker is to
know which form of information the game
embodies at any given timeand to keep track
of and predict how the game’s information
moves between these forms.
For example, a certain amount of the
information known only to one player (the hole
cards), can be leaked to other players due to that
player’s response to the turn, flop, or river (see
the Appendix for explanation of these terms). In
fact, in a recent study, Frey, Albino, and
Williams (2018) analyzed 1.75 million poker
hands and found that winning (skilled) poker
players were better than losing (unskilled)
players at integrative information processing
creating new information based on the
interaction between their own hole cards and
their opponents’ betting patterns. This made the
winning players’ decision-making less exploitable
and harder for others to reverse engineer (Frey et
al., 2018).
To recap, technical poker skills consist of
knowledge of hand strength and hand potential,
betting and bluffing strategy, ability to avoid
exploitation (playing unpredictably) and to
exploit others, and bankroll management; all of
which can be viewed in terms of challenges for
information manipulation. However, we note
that empirical research on technical poker skill
development in terms of information
manipulation strategies is still relatively scarce.
Emotion Regulation Skills
Due to statistical variance in the game, even
technically skilled poker players regularly
encounter losing streaks and “bad beats” —
losing money in situations where losing is
objectively unlikely, and not the result of
normatively poor decision-making. Losing large
sums of money often elicits negative emotions,
which, in turn, can have detrimental effects on
upcoming decisions. For example, in a bout of
anger an experienced and otherwise technically
skilled player might forgo sound betting or
bankroll-management strategies, ending up
playing with too high stakes and betting
erratically despite factually knowing it is
mathematically inadvisable. Thus, in addition to
technical skill elements, the concept of poker
skill encompasses an emotion regulatory aspect.
Emotion regulation skills refer to the ability to
withstand the arduous, yet inevitable, losing
streaks without having them affect the quality of
one’s decisions (Boujou et al., 2013; Palomäki
et al., 2013a). These skills may be conscious
processes explicitly controlling one’s impulses
by willpower or positive self-talk, or they could
be more unconscious processes, which might be
termed trait emotional stability or “character”
developed by surviving previous encounters.
McCormack and Griffiths (2012) interviewed
professional and recreational poker players and
found that professional players were not only
more likely to be logical and controlled in their
behavior, but also took fewer risks and were less
likely to chase after losses (i.e., keep playing in
an attempt to win back their losses). Conversely,
recreational players showed more signs of
losing control, taking unnecessary risks and
playing under the influence of intoxicants. In
correlational online studies, poker experience
has been found to be negatively associated with
the psychological traits of emotionality
(Laakasuo, Palomäki, & Salmela, 2014), self-
rumination (Laakasuo, Palomäki, & Salmela,
2016; Palomäki et al., 2013a) and emotional
sensitivity to losses (Laakasuo et al., 2016;
Palomäki et al., 2014). Experienced players are
thus less emotional, dwell less on negative
thoughts, and report higher emotional tolerance
of poker losses than do inexperienced players.
Moreover, Palomäki and colleagues (2013a)
report that in an online setting with simplified
poker tasks, experienced playersbut not
inexperiencedmade mathematically better
poker decisions when they had a strong
tendency for self-reflection. Self-reflection is a
trait related to analyzing one’s past mistakes in a
cool and detached manner. Consistent with
these results, Leonard and Williams (2015)
employed a measure of technical poker skills
and betting strategy and found that proficient
players were less susceptible to gambling
fallacies and had higher emotional tolerance for
financial risk and better social information
processing skills.
Palomäki et al. (2020) Poker as Domain of Expertise
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Journal of Expertise / March 2020 / vol. 3, no.2
Tilt: Intense Moral Anger Revealed in Poker
The importance of emotion regulation skills and
aversion to loss (via pursuing risk) in poker is
underscored by the phenomenon known as
tilting, which refers to losing control due to
negative emotionstypically related to bad
beats or prolonged losing streaksand making
strategically weak decisions and losing
significantly more money than otherwise
(Palomäki et al., 2014; Moreau, Delieuvin,
Chabrol, & Chauchard, 2017). Extreme cases of
tilting have even led to losing entire life savings
within minutes, and to self-reported memory
losses of the preceding events (Palomäki et al.,
2013b; Tendler, 2011).
Poker communities seem to agree that tilting
is a significant phenomenon: in an online study,
88% of poker players reported having tilted
severely at least once within their last 6 months,
43% more than five times, and 24% more than
10 times (Palomäki et al., 2014). Hence, this
form of “mental disarray” occurs with a
substantial frequency, leading to substantial
costs for those involved. These findings are in
line with the studies by Smith, Levere, and
Kurtzman (2009), as well as by Eil and Lien
(2014), who used big data on millions of played
online poker hands and found that players tend
to pursue risk when losing, but play cautiously
when winning. This effect is possibly driven by
emotional aversion to loss.
Social cues may also interact with emotional
reactions during poker decision-making: In an
online experiment employing a poker decision-
making task with mathematically defined optimal
choices, inducing the feeling of anger (via reading
emotional stories) reduced decision-making
accuracy. However, this effect was driven by a
social cue: displaying a pair of human eyes that
“followed” the participants’ mouse cursor during
the task (Laakasuo, Palomäki, & Salmela, 2015).
What leads to such costly lapses in
judgment? In a qualitative study on poker
players’ experiences of losing significant
amounts of money, tilting was characterized by
feelings of anger, frustration, and significantly,
injustice (Palomäki et al., 2013b; see also
Barrault et al., 2014). Social elements such as
unfriendly comments by other players often fuel
the negative emotional states leading to tilting
(Browne, 1989). The sense of injustice is
particularly interesting, as it makes tilting a
form of moral emotion: Individuals (sampled in
Palomäki et al., 2013b) who tilt reported feeling
personally insulted, and that they “unfairly” lost
money for which that had worked diligently.
They viewed variance as “bad luck,” took it
personally, and started pouring their earnings
into the game and chasing their “fair chance.”
The authors postulated (Palomäki et al., 2013b)
that the psychology of tilting could be viewed as
moral anger: Losing due to bad luck is
perceived as “cosmically” unjust, which
motivates an overly aggressive yet ineffective
retaliation strategy of excessive betting. In the
aftermath of tilting, the players reported being
disappointed in themselves and that they were
ruminating over lost resources.
Experienced players, however, differ from
inexperienced ones in their reporting of better
skills for regulating negative game-induced
emotions. Some experienced players in
(Palomäki et al., 2013b) reported that their
general emotion regulation skills had improved
through playing poker. These players also thought
that a clear understanding of mathematical
concepts, such as variance, is related to a mature
disposition towards encounteringbad luck
(“luck doesn’t exist, only variance does”
[Palomäki et al., 2013b]), which suggests that in
poker, emotion regulation skills and technical
skills are tightly intertwined.
Social Nature of the Game
In poker, the dynamics of social interaction
such as opponent characteristics or gender
effectsare crucial in understanding decision-
making quality. The social setting of the game
also plays a significant role in biasing poker
decisions on the one hand, and on the other
provides players with potentially accurate
information in the form of behavioral “tells”
(Caro, 2003).
Schlicht, Shimojo, Camerer, Battaglia, &
Nakayama (2010) employed a simplified poker
task and found that opponents whose facial
expressions displayed more trust were more
often folded (given up) against. The authors
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argued that by betting the opponent is implicitly
“sending a message” that he has a strong hand,
and, because he looks trustworthy, the message
is believed. In another study with a poker task
involving repeated decisions against the same
opponent, participants’ decisions were more
strongly influenced by their opponents’ prior
actions when the opponents were represented as
humans rather than as computers (Carter et al.,
2012).
In both of these studies, the human
opponents were presented as males. Poker
players seeking long-term engagement with the
game value masculine identities and player traits
(Wolkomir, 2012), and the vast majority (90-
95%) of poker players are male (Palomäki et al.,
2014; see also Abarbanel & Bernhard, 2012).
Also, poker decision-making itself seems to be
gender-biased: In an experiment using realistic
online poker tasks where opponents were
represented as either male or female avatars,
participants (of whom 93% were male) bluffed
6% more frequently at online tables with
female-only avatars compared with male-only
tables amounting to a significant difference over
time (Palomäki et al., 2016). A majority of the
participants also reported that the gender of their
opponents did not influence their decisions to
bluff, which suggests an implicit (unconscious)
bias in bluffing female opponents, who might
have been perceived as “easier” targets than
males.
Together, these results highlight the notion
that the social nature of poker is a key element
in fully understanding decision-making quality
and biases in the game. But turning it around,
poker is a tool to study decision-making and
socially driven decisions in a market
environment-like scenario, which, to date, has
received relatively little attention in research.
Measuring Poker Skill
Time, speed, or distance measures can be used
in many sports for objective quantification of
performance; and in chessthe game most
studied in cognitive scienceElo points provide
a high-validity measure of performance. In
poker, however, skill-level is often assessed
indirectly by self-reported experience or
simplified poker tasks, as previously discussed.
The element of chance obfuscates empirical
assessment based on earnings and calls for very
long observational histories. It would be better if
the probabilistic “goodness” of individual
players’ decisions (e.g., the expected value in
terms of monetary winnings) could be evaluated
based on a reasonable number of hands played.
The expected value of poker decisions can
be evaluated in simplified scenarios (see
Laakasuo, Palomäki, & Salmela, 2015; Leonard
& Williams, 2015). However, evaluating the
expected value of complex poker decisions “in
the wild” is extremely difficult, given all the
aforementioned cues potentially affecting (or
biasing) the players’ decisions and the element
of chance. One way to tackle this problem is by
benchmarking poker players’ decisions against
the best artificial intelligence (AI) poker
programs. Somewhat recently, an NLHE AI not
only won the 2016 Annual Computer Poker
Competition, but in 2017 defeated four highly
skilled professional poker players in heads-up
(one versus one) matches for about $1.8 million
over 120 thousand hands7. Poker AI has thus
been benchmarked against the highest human
standard and proven sophisticated enough to
beat the very highest-performing human players.
Therefore, these programs can act as a
normative reference whereby the performance
of sub-elite players at least can be evaluated.
This would be based on how well their decisions
correspond to the consensual decisions of the
best AI. To our knowledge, such efforts have
not been made yet, highlighting a potential
avenue for future research.
Development of Poker Expertise (RQ2)
Our second research question asked how poker
skills develop into expertise and how poker
allows for studying expertise. The complexity of
requisite technical knowledge in poker (as
explained in “Technical Skills,” above) is
evident even in a simplified poker decision-
making task, which we have provided in the
Appendix. Poker also lends itself well to be
examined under theories of expertise. Due to
having a chance component embedded in a
well-defined rule structure, poker even helps
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extend existing work on expertise to domains
where decision quality is not fully correlated
with observed outcomes (unlike in chess, for
example, where consistently making the best
decisions reliably leads to good outcomes).
Deliberate Practice
The deliberate practice (DP) framework is the
most established explanation for how expertise
is acquired (Ericsson, 2007; Kaufman &
Duckworth, 2017). It can be applied to study the
development and acquisition of poker skill,
expertise, and skilled intuition. In turn, the
special features of poker relating to chance,
emotion regulation, and social factors show that
acquiring mastery of only the technical aspects
of the game does not guarantee long-term
success. So far, the DP framework has been
used mainly in relation to what we have called
technical skill, and therefore we suggest that the
question of emotion regulatory skill
development (through DP or otherwise) is an
important new direction.
Within the DP framework, research on the
cognitive foundations of expertise has shown
that the superior performance of experts is not
based on general intelligence, but on a vast
amount of well-organized topic related
knowledge (Ericsson, Krampe, & Tesch-Römer,
1993; Ericsson & Lehmann, 1996; Ericsson &
Williams, 2007; Kaufman & Duckworth, 2017).
This knowledge is clearly acquired through
experience, and the DP framework characterizes
the nature of that experience by making one
core assumption: An individual’s level of
performance in the domain is monotonically related
to the amount of a specific type of practice (DP)
that person has engaged in. Put differently, the
attained level of expertise and performance are a
function of the time invested in DP. In music
training DP refers to (typically solitary) practice
to improve specific technical or artistic aspects
of one's skill, but not studying music theory,
public performances, or “jamming” (Ericsson,
Krampe, & Tesch-Römer, 1993). In chess,
studying and determining the best moves in
mid-game8 would count as DP, while merely
playing or spending time on studying the
literature generally would not. According to
Ericsson (2016), as a predictor of performance,
accumulated DP is more important than the
amount of overall domain experience, general
intelligence, or innate domain specific talent
combined (for further discussion, see Ackerman,
2014; Macnamara, Hambrick, & Oswald, 2014;
Macnamara, Moreau, & Hambrick, 2016; and
Hambrick et al., 2014).
Technical Poker Skill Acquisition via Deliberate
Practice
Although poker does not have a formal teaching
culture like in classical music and professional
sports, the range of self-coaching strategies
suggests that the online poker sub-culture is a
mature culture of expertise. A common
recommendation for “serious” novice players
seeking to improve their skills is to use poker
analysis software, which allows for monitoring
of session-by-session statistics on profit or loss
and betting strategy (Billingham et al., 2013).
After each session, the players can then
carefully analyze how they played and what
they could have done differently. Poker players
actively interact over virtual communities to
scrutinize poker strategy. Skilled players, in
particular, frequently post detailed breakdowns
of how they played for general discussion
(O’Leary & Carroll, 2013). Their aim is to fine-
tune their mathematically informed strategic
decisions in poker (Parke & Griffiths, 2011).
We posit that in poker, this type of study of
betting strategies in specific game situations
would count as DP for technical skills (we are
not aware of specific practice forms that would
be geared toward emotion regularity skills, that
is, DP for non-technical skills, in poker).
Although this is not solitary practice designated
by a teacher, the explicit goal of improving
specific skills and the setting-up of clear
feedback mean the process can be viewed as
DP, in the context of poker.
Moreover, posting one’s poker hands
(breakdown of a string of decisions within a
specific hand) for analysis and scrutiny on
online poker forums has three characteristics of
DP. First, a clear task structure, wherein the
players often receive step-by-step walk-throughs
on why certain decisions should or should not
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be made. Such walk-throughs may also isolate
subtasks, such as focusing on different stages of
the hand (e.g., decisions on the flop, turn, or
river). Second, there should be clear goals for
the players who aim for self-improvement. For
example, the feedback generated by playing the
game might be positive for bad decisions
(winning money despite making a decision with
negative expected value), but posting such
situations online for scrutiny helps players
discover the actual goodness of their decisions.
The proximal goal for players who seek
feedback on their decisions is often not merely
to enjoy poker or winning since they also post
hands where they have won but are uncertain
whether they played correctly9. Finally, there is
the element of diligent repetition, as players
who strive to get better keep posting poker
hands for scrutiny, which, in turn, helps them
increase their skills.
It should also be noted that because poker is
a competitive game, skilled players might have
an incentive not to help novice players to
improveor even an incentive to hinder their
progress. Novice players aspiring to get better
thus sometimes need to discern between misleading
and accurate information disseminated in online
poker forums (Talberg, 2019), as aspect of social
skill learning.
Emotion Regulation Skill Acquisition
The consensus is that technical poker skills can
be learned via practicing and studying the game.
However, studying poker alone is probably not
enough to learn and improve one’s emotion
regulation skills, because it is not easy to
“simulate in training” the loss of significant
amounts of money.
Traits such as low emotionality and low
tendency to self-ruminate are largely (possibly
innate) predispositions that enable some people
to become good players; namely, those who can
endure the stressful learning period as well as
the unavoidable losing streaks. Personality, IQ,
and other psychological traits, when measured
with standard psychometric instruments, are to a
large extent stable across time, and may be
difficult to alter systematically through practice.
However, the malleability of such traits, and the
directions of causality between poker skill
development and various psychological
characteristics could be fully evaluated only by
employing a longitudinal study design, where
poker players’ behavior is measured over
extended periods of time. To our knowledge, no
such study currently exists and would thus offer
a fruitful line for future research.
There is, however, a rich corpus of poker
self-coaching textbooks that focus on improving
one’s mental game skills. The authors of these
books typically recognize emotion control as a
highly significant element in poker skill
development (e.g., Angelo, 2007; Taylor &
Hilger, 2007; Tendler, 2011). Similar anecdotal
evidence has emerged from Esports, where
professional teams and individual players have
been significantly more successful in tournaments
after hiring sports psychologists specializing in tilt-
management (theScore esports, 2019).
Tendler (2011) draws from his experience as
a clinical psychologist working extensively with
poker players and offers detailed guidelines and
techniques for players to improve their tilt
control. He views poor tilt control in poker as an
issue of consistency in individual performance
level. Players perform better on some days than
on othersand the overall distribution of
performance level forms a bell curve around the
average performance level for each player. For
players with poor tilt control, this distribution is
wide, reflecting a large difference in
performance level between their best and worst
possible performance. Players with proficient
tilt control, in turn, have narrower performance
level distributions. In other words, their
performance is more constant across timethey
play almost as well on their “worst day” as they
do on their “best day.”
It is important to note that we do not claim
emotion regulation is an important “sub-skill”
only in the game of poker. It probably has wide
relevance across a range of domains, especially
those dealing with risk and uncertainty. We are,
however, proposing that the role of emotion
regulation becomes more pronounced in poker
than most domains that have been used in
cognitive science to study the nature and
development of expertise. In other fields such as
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chess or playing a musical instrument,
proficiency in emotion regulation (or staying
cool under pressure) might be what separates
the “super-elites” from the “merely experts.”
However, it is almost unheard of that poor
emotion regulation skills would cause a chess
grandmaster to lose against a beginner, or a
professional musician to fail to perform above
the novice level. In poker, however, tilting can
cause an otherwise technically proficient player
to perform extremely poorly. This is illustrated
in Figure 2, where emotion regulation skills are
conceptualized to affect within-individual
variability in performance over timeor, in
other words, performance level consistency
for individuals of putatively identical level of
technical skill.
Figure 2. Hypothetical role of emotion regulation (ER) skills in Music, Chess, and Poker among players
with high technical skills (“on their best day”) in their respective games. The lines depict the hypothetical
within individual variation across time in level of performance (i.e., consistency in level of performance)
for individuals with (1) low ER skills in Music, Chess, and Poker (dashed lines), and (2) high ER skills in
any field. For example, a technically proficient Poker player with low ER skills might sometimes perform
as well as those with high ER skills, but due to high variability in their performance level, they sometimes
perform as poorly as an amateur. This is not the case for technically proficient individuals with low ER
skills in Chess or Music, where performance level almost never drops significantly low. Note that for
simplicity, we assume that for good ER skills the profile of performance variability is the same across all
fields. We also note that this is a conceptual model; the level of individual performance with respect to
technical and ER skills in real life is likely somewhat more complex and nuanced.
Thus, some poker players who have
acquired a high level of technical skills (e.g.,
through years of DP) might still struggle with
having highly inconsistent performance levels
(dashed line for Poker in Figure 2). For these
players, technical skills alone are not enough to
reach a high average performance level. The
extent to which emotion regulation skills can be
learned, and if DP would be a suitable
framework to understand learning them, is
unclear and an important venue for future
research.
Skilled Intuition as the Interplay of Technical and
Emotional Skill
A corpus of anecdotal evidence suggests that
since the poker environment is complex and fast
paced, players need to trust their intuitions or
“gut feelings” when making a decision (e.g.,
Brunson, 2005; Tendler, 2011). These feelings
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can also be called affective heuristics (Finucane
et al., 2000) —that is, “unconscious” processing
of task-relevant information experienced
phenomenologically as good or bad “feelings”
about a situation. It has been empirically
established that chess masters, too, often rely on
an intuitive “feel” for different moves and
assessment of the “board as a whole,” especially
in the mid-game where options for various
moves are astronomical (e.g., Chassy & Gobet,
2011; Gobet & Chassy, 2009) and it is futile to
attempt to work through the alternatives step-
by-step in working memory.
Cognitive modeling work suggests that chess
masters’ intuition relies on pattern recognition
(“chunking”). Their intuition is a cognitive process
involving implicit memory and fast and automatic
procedural knowledge: the present board
configuration is compared to a vast knowledge-
base of mid-game positions encountered over
uncounted chess matches, analysis of chess
literature, and thousands of hours of playing chess
and solving chess problems (Gobet & Chassy,
2009). This implicit information processing seems
to also be accurate enough to assist in complex
decision-making in familiar domains. What would
this type of “skilled intuition,” or more specifically
implicit domain memory retrieval that benefits task
performance, look like in poker? We illustrate this
with a quote from a two-time World Series of
Poker main event champion, Doyle Brunson
(Brunson, 2005, p. 542):
Whenever I . . . feel . . . I recall
something that happened previously.
Even though I might not consciously do
it, I can often recall if this same play . . .
came up in the past, and what the player
did or what somebody else did. So,
many times I get a feeling that he’s
bluffing or that I can make a play and
get the pot. [My] subconscious mind is
reasoning it all out.
In this quote Brunson clearly alludes to what
can be called skilled intuition in the domain of
poker, manifesting in episodic memory recall or
gut feelings. The “feel” is, presumably, a
subconscious recollection of something that has
happened in the past, which cannot be
articulated in more detail. In cognitive terms,
skilled intuition can be viewed as a hallmark of
expert decision-makers across many domains,
but it can reliably exist only in environments
with stable relationships or regularities between
identifiable cues and specific events, actions and
outcomes, such as chess (Kahneman & Klein,
2009). These kinds of regular environments and
games are known as high-validity environments.
The opposite are low-validity environments,
such as changes in political climates, where
predictability of long-term outcomes from past
performance is limited, and any intuition-based
judgment is likely to be flawed or biased
(Kahneman & Klein, 2009). The higher the
validity of the environment, the better the
chances are for acquiring skilled intuition in that
environment (e.g., chess, bike riding, or the
game of djenga). DP may be seen as a means to
increase the validity of (some aspects of) the
environment.
Is poker a high-validity environment? Given
the strong element of randomness, specific
decisions consistently lead to specific outcomes
only over the long run. Learning poker strategy
is therefore challenging because the process is
masked by outcome variability (Figure 1). In the
short run, players will often receive positive
feedback from bad decisions, which may elicit
an illusion of skill (Bjerg, 2010), and vice versa,
obfuscating actual skill. Even after many hours
of practice and play, players might have an
erroneous conception of their true skill, and the
soundness of their choices. Indeed, MacKay and
colleagues (2014) found that increased
frequency and duration of poker play was more
strongly associated with online poker players’
perceived skill than with their objectively
measured skill. There are tools to measure one’s
level of skill objectively in chess (Elo-ratings)
but not in poker; due to this poker players are
also more biased in predicting their individual
success in tournaments (Park & Santos-Pinto,
2010). On the other hand, poker is based on a
deterministic system of rules, such that it is
predictable at some scale. Thus, pokeror any
other similar game where the goodness of
decisions is defined only over the long run
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might be considered a medium-validity
environment.
To our knowledge, no empirical studies
have evaluated differences between medium-
and high-validity environments in the
development of expertise and skilled intuition. It
is also unknown to what extent and what aspects
of emotion regulation skillswhich are probably
more idiosyncratic to medium-validity
environmentsbecomeintuitive” (when
“emotional maturity” in the face of losses is
achieved), and to what extent they require constant
cognitive control. During prolonged series of
losses, some playerseven self-proclaimed poker
professionalsmay start thinking the game is
“rigged” against them. In other words, technically
skilled players may start believing that the online
poker sites deliberately manipulate who gets to win
and who does not (Tendler, 2011; Paloki et al.,
2013b), even when these beliefs are not supported
by evidence. Such experiences are likely more
frequent in medium-validity environments than in
high-validity environments (and most frequent in
low-validity environments, where all kinds of
irrational beliefs and “superstitionsmay develop).
Poker as a research tool also offers us the
chance to contrast with existing results, for
example that chess masters employ different
evaluation strategies to novices; they mentally
falsify their hypotheses rather than confirm
them as novices do (Cowley, 2017). Such
comparative work would shed light on the
processes by which reduction of validity affects
expert decision-making strategies also for
experts.
Poker as a Research Tool (RQ3)
Let us recap where we are, theoretically, before
we proceed to consider specific ways poker
could be used as a research tool in the study of
expertise. We started by analyzing the
probabilistic aspects of typical poker decisions
and described the information structure of the
game. We then progressed to show how this
task environment is modulated by several
different factors and addressed the issues related
to poker skill conceptualization. Next, we
placed poker within the framework of expertise
and deliberate practice and suggested that
performance in poker could be largely driven by
skilled intuition: Technical poker skills should
not be construed just as the ability to perform
explicit mental calculations, but also as the
ability to make skilled intuitive judgments based
on a “feel” for the game—as is also the case in
more established expert domains. However,
skilled intuition or gut feelings in poker may be
hard to obtain due to natural outcome variability
in the game (mathematically good decisions
that is, decisions with positive expected
valuesmight not result in preferred outcomes).
Also, since the poker decisions become
intuitive, they are in danger of being interfered
with by external factors, such as emotions of
social anger and “tilting,” as well as gender
stereotypes. Therefore, a crucial aspect of
becoming good at poker is developing skills for
regulating one’s emotions in the face of
stochastic outcomes, in a challenging social
environment. Based on this framework of
understanding of poker, we are now in a
position to illustrate some of the potential that
poker holds for research on decision-making
and expertise.
Generally, poker seems to be better posed
for longitudinal studies than many other
ecologically valid games, or purely game
theoretical lab-games, since in poker the
concept of skill has an important and well-
defined meaning mainly over the long run and in
the context of emotional tolerance of variance
(Palomäki et al., 2013ab; Palomäki et al., 2014;
Laakasuo et al., 2014). For example, the amount
of DP in poker may not strongly reflect players’
long-term success unless they also invest in
mental game training, which, in turn, may or
may not be achievable via DP (see Figure 2).
Future studies should thus look into how
effective DP is the context of developing
emotion regulation skills, or “mental toughness”
across various fields (e.g., Tendler, 2011).
Another route for future studies is evaluating
motivational factors in developing poker skills.
Some research suggests that a masculine identity
is very important for poker players who seek long-
term engagement with the game (Wolkomir,
2012). However, we have little knowledge of how
different identity factors contribute to possible
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biases or errors in economic decision-making.
Research on this topic is prima facie interesting
and relevant to understanding of, for example,
stock market trading or risky decision-making in
general. If poker for some players is about
pursuing “male glory” (Palomäki et al., 2016;
Wolkomir, 2012), is it also about “manly risk
taking”? Do poker players with stronger
masculine identities make riskier decisions, and
when are riskier decisions better decisions? Since
poker involves risk, it would also be useful to see
how risk-taking, masculine identity and emotional
volatility react with and possibly hinder rational
decision-making. Whether or not people make
risky decisions in economics and sports for
reasons of fame and glory is interesting for several
reasons. Do we want egoistically-motivated stock
traders, or leaders who take risks to boost their
own self-image, if these motivations make them
blind to disastrous outcomes?
Poker research seems to have uncovered a
previously unstudied emotional state called “tilt”
a specific type of moral angerwhich could
possibly also be found in other areas of decision-
making and expertise, such as sports or finance.
What is going on in other domains of action when
people lose control; for example, in stock market
trading, golf, tennis, or racing (Wei et al., 2016)?
The term “tilt” has also been adopted into
common use in the world of online gaming and
Esports (theScore esports, 2019). Is tilting a
uniform phenomenon across of these domains; if
so, how much of expertise within these domains
depends on emotion regulation skills?
There seems to be a zen-like quality in top
poker players who report not getting anxious
about the “swings” of their fortune in poker
(Palomäki et al. 2013b), similar to the skill of
experienced investors like Warren Buffet. Can we
find in other domains similar results regarding
emotion-regulation skill: Namely that self-
reflection, emotional stability, and understanding
“variance in life” (a que sera, sera-type stoic
mentality) protects against destructive emotions?
Players could be taught emotion regulation skills
via, for example, mindfulness meditation (which
has shown promise in improving emotion
regulation in a gambling context [de Lisle et al.,
2012]); meditation-based intervention might
improve poker players’ decision-making.
Pinning down and measuring the elements
comprising poker skills would also be informative
in the study of skilled intuitions and their
acquisition. The role of skilled intuition, or gut
feelings, in poker decision-making has not been
empirically investigated. At what level of skill do
gut feelings start being accurate enough to aid in
decision-makingor in other words, when will
poker players start profiting from listening to their
intuition instead of losing because of it? In poker,
it is difficult to accurately estimate one’s own
skill, since the observed outcomes of playing are
masked by variance. This creates extra pressure on
players to deliberately self-reflect on their session-
by-session decisions without focusing too much
on the actual results. These questions offer a
fertile and significant area for study that will serve
to further integrate research on emotions and
decision-making. Table 2 presents our
conceptualization of poker skill and its sub-
components.
Table 2. Conceptualization of poker skill
Poker skill
Technical
Emotion regulation
Understanding
probabilistic dependencies, chance, and variance
hand strength and hand potential concepts
bankroll management, betting strategy
opponent behavior
Tolerance for losses and “bad beats”
Responding to “swings”
Avoiding loss of control and “tilting”
Intuitive
Implicit assessment;
affective heuristics (“gut
feelings”)
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Conclusion
Poker offers an ecologically valid, rule-based, and
well-structured environment of decision-making
under risk and uncertainty, where decisions are
made under emotional pressure in a social setting,
and on the basis of substantial domain knowledge
and skill. Thus, studying poker not only sheds
light on human decision processes, but also on
how skill and expertise in these processes develop
with experience, and how social and emotional
factors moderate such decisions.
Most traditional tasks used to gain knowledge
on human decision-making are, in contrast,
relatively simple, numerically presented, and
administered in a laboratory setting. They have
debatable ecological validity and may not
accurately model how humans behave in more
complex, naturalistic real-world settings.
Moreover, the participants are almost always
inexperienced in these tasks, and thus it is also
very difficult to model how expertise would
moderate any observed effects. The expertise
literature, on the other hand, studies tasks and
skills that are measured across domains in the real
world and thus have high ecological validity, but
also complexity, making them difficult to
operationalize, or to determine the a priori
normative decisions. Taking advantage of
“naturally occurring” laboratories,
such as poker, stands to greatly benefit the study
of decision-making and expertise.
Endnotes
1. We define expertise as the ability to reliably
and consistently produce a level of
performance, in a specific domain, that is
much superior to the level attained by the
novice.
2. Even if poker has not been extensively studied
in the area of decision-making, historically it
has been important: the game is said to have
inspired John von Neumann to invent game
theory (von Neumann & Morgenstern, 1944).
3. In online poker, there is typically a time limit
of 1-3 minutes per decision. In live poker,
time constraints are not as obvious as in online
poker. Nonetheless, taking “too long” to make
a decision is considered bad table etiquette
(Malmuth, 2012). Moreover, in live poker,
players are allowed to ”call the clock” on any
other player (at any time), at which point the
said player typically has 60 seconds to act
until his/her hand is declared “dead” (i.e.
automatically folded). These rules depend on
the casino where poker is played.
4. For more details, see the Appendix; for the
general rules of poker consult Krieger and
Bykofsky (2006).
5. More extreme win rates do not change this
picture: Variance is not affected by the
degree of win rate.
6. Nash Equilibrium in poker is when two
players are playing a strategically “optimal”
game (in terms of expected value) against one
another, and neither can gain anything by
unilaterally deviating from the said “optimal”
strategy (Bowling et al., 2015).
7. See http://www.computerpokercompetition.org
and https://www.theguardian.com/ technology/
2017/jan/30/libratus-poker-artificial-
intelligence-professional-human-players-
competition
8. Typically, middle game in chess is considered
to begin when both players have completed
the development of all or most of their pieces
and the king has been brought to relative
safety.
9. Note that this does not include situations
where the player has won with an inferior
hand after the odds are explicitly known that
is, due to “good luck. Rather, here we refer to
situations where, for example, the player
decides to bluff and the opponent folds. In this
case bluffing might actually have been
incorrect, if the probability of the opponent
folding was too low.
Acknowledgments
The authors thank Jane and Aatos Erkko
Foundation (grant no. 6-5291-3 to ML [PI] and
JP), Emil Aaltonen Foundation, the Finnish
Cultural Foundation (grant no. 00150514 to OL),
and the Academy of Finland (grant no. 325694 to
JP) for financial support. They also thank Drs.
Jami Pekkanen, Jukka Sundvall, Eeva Palomäki
and Nils Köbis for feedback on the manuscript.
Palomäki et al. (2020) Poker as Domain of Expertise
https://www.journalofexpertise.org
Journal of Expertise / March 2020 / vol. 3, no.2
Author’s Declarations
The authors declare that there are no personal or
financial conflicts of interest regarding the
research in this article.
The authors declare that they conducted the
research reported in this article in accordance with
the Ethical Principlesof the Journal of Expertise.
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Palomäki et al. (2020) Poker as Domain of Expertise
https://www.journalofexpertise.org
Journal of Expertise / March 2020 / vol. 3, no.2
Appendix
Task Analysis of a Poker Decision
This analysis of the most popular game variant of
poker, namely No Limit Texas Hold’Em
(NLHE), 1) outlines the game’s rules and
strategic fundamentals, and 2) illustrates the
cognitive complexity of the game.
The goal in NLHE, like any other poker
variant, is to have the best combination of cards
in comparison to the other players on the table. In
NLHE the sequence in one round of play goes as
follows. First, during so-called “pre-flop,” two
cards are dealt to each player (“hole cards”);
these cards can be seen only by the respective
player. Next, five commonly shared cards
(“community cards”) are sequentially dealt to the
middle of the table for everybody to see. The first
three community cards are known as “the flop”
and dealt simultaneously. The following two
community cards are known as the “turn” and
“river,” and dealt separately. These community
cards in combination with the hole cards
determine the winner of the round. To be more
precise, the players can use one or both (or none)
of their hole cards in combination with the
community cards to form the best five-card
combination, following a hierarchical ordering of
values of all five-card hands.
There are, at most, four rounds of betting: (1)
during pre-flop, (2) after the flop is dealt, 3) after
the turn is dealt, and 4) after the river is dealt. The
betting has the following structure. Players can
either “check” (not bet while not giving up),
“bet” (invest money in the pot), “call” (match an
opponent’s bet), “raise” (go beyond an
opponent’s bet), or “fold” (give up and exit the
round). Once a full round has been played this is
considered as having played one “hand.”
To illustrate the logic of the game let us
examine the situation outlined in Figure A.1.
Your (“YOU” in Figure A.1) current best five-
card hand is called king high, which is considered
a very weak hand. However, one more
community card (the river) will be dealt if neither
player still in contention gives up (folds). Hence,
there is another chance for you to improve your
hand. One way to improve is when the river card
is either a 4 or a “9”; then you would have a
straight, which is the best possible hand given the
current community cards. Deciding whether to
stay in the game, or not, is influenced by the
likelihood of this event occurring. In this case,
the odds of your hand improving into a straight
on the river is about 17% (at maximum 8 cards
from a total of 42).
Let us assume that you are highly skilled and
decide to pass the turn to the Opponent (check).
The Opponent bets $100 into the pot of $135
(making the pot $235 in total). Now you know
that to be guaranteed to win (disregarding ties for
simplicity) your hand needs to improve into a
straight on the river. Matching the opponent’s bet
(calling) of $100 when the size of the pot is $235
increases the pot to $335. This corresponds to
immediate odds of 2.35 to 1 (or 100/335 =
29.8%), which means that calling would be
profitable if it were the winning decision 29.8%
of the time. In simpler terms, one would need to
be in a similar situation at least 2.35 times for the
same decision to have a positive outcome.
Since your hand will improve into a straight
on the river only about 17% of the time, it follows
you should not call based on the immediate odds
alone (17% < 29.8%). However, you also know
that if you call and improve your hand into a
straight, you might win additional money by
making the pot largereither by betting yourself
or by “inducing” a bet from the Opponent by
checking. Thus, you should consider also your
so-called implied odds; that is, what calling now
might imply later in terms of profit.
Whether the implied odds justify calling
depends on the Opponent’s strategy and the hand
the Opponent is holding. For instance, if the
Opponent is unskilled it might be rational to take
the chance of playing despite the poor immediate
odds, because unskilled players are more likely
to make mistakes and “pay off” bets on the river
when they should not. In other words, even if a
certain card combination would be statistically
unlikely to win, in certain situations it might still
make sense to play them.
In poker, players have only probabilistic
information on how to act and need to rely on
previous experience and reasoning skills to make
their next decisions. This process involves
estimating all of the possible card combination
the Opponent is expected to have (“hand range”),
Palomäki et al. (2020) Poker as Domain of Expertise
https://www.journalofexpertise.org
Journal of Expertise / March 2020 / vol. 3, no.2
given the community cards and the Opponent’s
betting actions previously (and body language in
live poker; or chat comments in online poker, and
so on).
In the situation outlined in Figure A.1, you
can estimate how “strong” your own five-card
hand is against the “average strength” of the
Opponent’s hand range. This estimation is
sometimes done quickly and implicitly, because
time pressure alone often prevents explicit
detailed calculations skilled players sometimes
play on multiple tables online, some on as many
as 24 at a time (e.g., Rhodes, 2010).
The analysis above is an oversimplification,
and merely illustrates the complexities in poker
decision-making. You as a player in the game
should also consider bluffing on the river, if your
hand does not improve. Also, you could decide
to bet initially, or raise the Opponent’s initial bet
after checking. These would entail new
probabilistic dependencies, which we have
omitted. While this task analysis is hypothetical,
it is an empirical question how explicitly
analytical (or intuitive) players’ cognitive
processes are in similar situations. Determining
opponents’ hand ranges in various situations is
discussed across poker communities (O’Leary &
Carroll, 2013).
Figure A.1. An online NLHE table (adapted from Palomäki et al., 2016). A: Opponents 1, 3 and 4 have folded
(given up) and are no longer contesting the pot. B: The total amount currently in the pot, which represents all the
money that has been previously waged during the current hand. C: The amount of money the player has remaining
in their stack, which represent the total amount they will be able to wage during any particular hand. D: The “hole
cards” of the Player, not visible to the opponents. E: The “hole cards” of the remaining opponent. F -H: The
“community cards” shared by the player and the opponent. F: The flop (three first “community” cards). G: The
turn (the fourth community card). H: The river (the fifth and last community card to be dealt, at this point
unknown).
... This may be due (in-part) to the incorporation of real-world information that engenders a sense of self-agency within these gambling modes. Poker players typically learn to 'read' the environment and the behaviours of their opponents whilst making economically underpinned decisions [26]. Similarly, sports bettors often utilise team/player/match statistics whilst evaluating odds-related information when placing bets [8]. ...
... Similarly, sports bettors often utilise team/player/match statistics whilst evaluating odds-related information when placing bets [8]. Corresponding research evidence indicates that skill, knowledge, and expertise within both poker and sports betting may increase the frequency of successful bets [24,26]. However, whether or not this initial advantage leads to an increase in tangible winnings is less clear due to the cyclic nature of the game mechanics that may encourage the re-staking of winnings [24,26,27]. ...
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[Findings from Ch.2 and Ch.3 appear in latest edition of Michael Eysenck’s world-leading psychology textbook 'Cognitive Psychology: A Student’s Handbook 2020'; Select findings from Ch. 4 appeared in Nature News Aug 2004, the 'Cambridge Handbook of Expertise & Expert Performance 2018 and Experimental Psychology Oxford Seminar Series 2010. Funded by IRCHSS and Oxford Law Faculty Fund]. Each day people are presented with circumstances that may require speculation. Scientists may ponder questions such as why a star is born or how rainbows are made, psychologists may ask social questions such as why people are prejudiced, and military strategists may imagine what the consequences of their actions might be. Speculations may lead to the generation of putative explanations called hypotheses. But it is by checking if hypotheses accurately reflect the encountered facts that lead to sensible behaviour demonstrating a true understanding. If evidence shows a hypothesis to be false, then people should rationally abandon it, especially if there are negative consequences. The aim of this thesis is to examine how effectively people search for evidence in their hypothesis testing to test whether or not their hypotheses are true or false in competitive games. Research findings from six studies of hypothesis testing behaviour in competitive deductive tasks are explored. Chapter by chapter the thesis tests how everyday people, and master chess players, tackle hypothesis testing in mathematical tasks, such as how to solve sequential number sequence puzzles when thinking about an opponent, or how to solve chess problems in a variety of contexts. The implications of the results are discussed in light of aspects of general cognition: such as reasoning, social hypothesis testing and planning.
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