Abiding Chance: Online Poker and the Software of Self-Discipline

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Abiding Chance:

Online Poker and the Software

of Self- Discipline

Natasha Dow Schüll

A man sits before a large desktop monitor station, the

double screen divided into twenty- four rectangles of equal size, each containing

the green oval of a poker table with positions for nine players. The man is virtu-

ally “seated” at all twenty- four tables, along with other players from around the

world. He quickly navigates his mouse across the screen, settling for moments

at a time on ashing windows where his input is needed to advance play at a

given table. His rapid- re responses are enabled by boxed panels of colored

numbers and letters that oat above opponents’ names; the letters are acronyms

for behavioral tendencies relevant to poker play, and the numbers are statistical

scores identifying where each player falls in a range for those tendencies. Taken

together, the letters and numbers supply the man with enough information to act

strategically at a rate of hundreds of hands per hour.

Postsession, the man opens his play- tracking database to make sure the

software has successfully imported the few thousand hands he has just played.

After quickly scrolling through to ensure that they are all there, he recalls some

particularly challenging hands he would like to review and checks a number

Public Culture 28:3  10.

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ESSAYS

Thanks to Paul Rabinow and Limor Samimian- Darash, for prompting me to gather this material

for a different article, and to Richard Fadok, Paul Gardner, Lauren Kapsalakis, and the students in

my 2013 Self as Data graduate seminar at the Massachusetts Institute of Technology, for helping me

to think through that material. Thanks also to the organizers and audience members of New York

University’s Cultures of Finance group, especially Arjun Appadurai, Benjamin Lee, Randy Martin,

Caitlin Zaloom, and Robert Wosnitzer, and to the organizers and participants of the 2014 “Calculat-

ing Capitalism” workshop at Columbia University, especially William Deringer, Paolo Quattrone,

and Dotham Lesher. Finally, thanks to Josh Berson, Rodrigo Ferreira, and Frank Lantz for helpful

comments in the revision stage.

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of lters to reveal for further analysis only hands that match these criteria.

While replaying the hands forward in simulations to see how different actions

might have played out, he runs a statistical analysis to determine whether his

performance for the session matched performance expectations for the cards

he was dealt and, if not, whether the deviation has to do with skill or luck. He

consults a graph of his “aggression factor” to convince himself that he hasn’t

been playing as timidly as he used to and, nally, makes some notes in an Excel

spreadsheet on minor behavioral adjustments to apply during his next session.

Satised that he has taken adequate inventory of his game performance that day,

he closes the program without once checking to see how much he won or lost; now

is not the time to be misled by short- term data.

.........

“I wish I was a robot,” the much- admired live- poker player Jennifer Harman once

confessed to a journalist, explaining how hard it was to act, in any given moment,

according to the statistical laws that she knew, rationally speaking, she should

trust (Glass 2001). What Harman fears is “tilt,” a term deriving from pinball that

gamblers use to describe the shaken emotional state they are liable to enter during

the course of a game.

When on tilt, gamblers inate the signicance of short-

term events and lose sight of the long- term horizon, along with the ability to make

decisions wisely — that is, in accordance with the law of large numbers. To keep

their destructive in- game passions at bay, poker devotees like Harman resort to

various rules, techniques, and codes of conduct.

Those who play poker online are in special need of tilt- avoidance tools, for

the likelihood of tilting increases, as do its costs: if players tilt in a live game,

they can sit out a couple of hands to clear their heads without great consequence,

but if they tilt online, the effects quickly bleed over to the other tables they are

simultaneously playing at, linking the tables in a dangerous cascade of emotional

reactivity.

With the rise of online poker has come an impressive array of digital

tools designed to help players maintain equilibrium. Unlike tools for tilt avoid-

ance in live play, these rely on continuous data- gathering and microcomputational

analytic algorithms, offering gamblers “digital insight” (Hansen 2014: 24) into

1. When a pinball player shook a machine too roughly (to move the ball where he or she wished

it to go), its tilt sign would light up and the game would end.

2. “Each hand interlock[s] with the next,” wrote the author of a 2006 prole of online poker

addiction (Schwartz 2006: 55). “Time slows down to a continuous present, an unending series of

build- ups and climaxes. The gains and losses begin to feel the same” (ibid.). For an extended account

of how digital media contributes to the experience of gambling addiction, see Schüll 2012.

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3. In The Taming of Chance, Ian Hacking (1990) shows how the notion of pure randomness

that had emerged from experiments with games of chance in the seventeenth century was tempered

the unfolding dynamics of play. They have become such an integral part of online

poker play that it is a near requirement for serious players to use them.

The anthropologist Thomas M. Malaby (2003: 147) has described gambling

as “a semibounded refraction of the precarious nature of everyday experience, a

kind of distillation of a chanceful life into a seemingly more apprehensible form.”

Online poker, I argue, performs this work of refraction, distilling certain features

of “chanceful life” into a computational format that players can interact with,

experiment with, and, sometimes, learn to abide. Drawing on interviews with

gamblers, observations of online poker play, and discussion threads from poker

forum archives, this article explores how the game offers players a training ground

in how to act decisively in a world where “contingency, risk and indeterminacy

have become predominant” (Arnoldi 2004: 36; Luhmann 1998: 95).

Numerous contemporary theorists have recognized choice making under condi-

tions of uncertainty as a dening predicament of the present. “Everyday risks pres-

ent us with the necessity of making a seemingly never- ending set of choices,” writes

Alan Hunt (2003: 169). “Modern individuals are not merely ‘free to choose,’ ” elab-

orates Nikolas Rose (1999: 87), following Anthony Giddens (1991), “but obliged

to be free, to understand and enact their lives in terms of choice.” Alberto Melucci

(1996: 44), in The Playing Self, similarly describes choosing as “the inescapable

fate of our time.” I approach online poker as an arena in which players grapple

with this fate, examining how they engage an array of digital media — including

real- time data tracking, dynamic numerical displays, statistical visualizations and

retrospective simulations, analytic algorithms, and chat forums — to render the eld

of uncertainty apprehensible, available, and actionable.

The uncertainties that arise in the course of play are multiple, each unfolding

from the next in an ever- complicating cascade: What cards are others holding?

How might they play those cards? What cards do they suspect you of having and

how do they believe you are likely to play them? Are they tracking you as you are

tracking them? If so, how will the actions you take affect their statistical models

of your behavior? As one might gather from the scene recounted above, the appar-

ent purpose of poker tracking and analysis tools is to reduce and even neutralize

such uncertainties, and yet, from another perspective, these tools can be seen to

multiply and galvanize uncertainty by continuously tracking the data of chance

events, ltering that data through rapid- re statistical algorithms, and transmitting

it back to the gambler in distilled, digestible form — just in time for the next action.

In this way, they seek to game chance rather than to tame it.

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The propagation and digital rendering of aleatory events in online poker also

performs a less obvious, less strategic function, which is my focus in this arti-

cle. Namely, it affords gamblers the opportunity to cultivate — through the use

of its chance- distilling features and the development of personal routines of self-

inventory and self- adjustment — an attitude of subjective equanimity in the face of

uncertainty. Practiced exposure to a digitally mediated stream of chance lowers

their risk of becoming emotionally swept up in the volatile unfolding of game

events and falling into the dreaded state of tilt. As we will see, the composure

toward events- in- time that gamblers cultivate online carries over to life off- line,

lending them a subjective “readiness” for living with uncertainty. In this sense, the

digital tools available to online poker players can be understood as technologies of

the self, famously described by Michel Foucault (1997: 225) as those “which permit

individuals to effect by their own means or with the help of others a certain number

of operations on their own bodies and souls, thoughts, conduct, and way of being,

so as to transform themselves in order to attain a certain state of happiness, purity,

wisdom, perfection, or immortality.” Although digital media is often associated

with effects such as choice paralysis and the disappearance of the subject, the case

of online poker demonstrates that it can also serve as a vehicle for self- fashioning.

To explore this aspect of the game, I entertain the possibility of certain afnities —

some of them unlikely, on rst consideration — between the practices of online

poker players and those found in a range of ascetic traditions including the self-

vigilant monitoring of the Greco- Roman Stoics, the Christian monastic arts of

world renunciation and self- purication, the elaborate spiritual accounting exer-

cises of the Jesuits, the self- scrutinizing journals of the Puritans, and the Eastern

arts of meditation and yoga. My intent in drawing on this array of diverse exam-

ples is not to atten the signicant differences among their respective cosmologi-

cal visions, moral strictures, and spiritual aims — or to suggest that the secular-

ized self- exercises of online poker amount to a modern- day asceticism (except in

the etymological sense of askesis as “training”). Rather, it is to gain perspective

on the methodical self- regulative experiments, protocol- following behaviors, and

by the rise of statistics in the nineteenth century. As worldly phenomena came to be understood

as governed by statistical laws, chance was “tamed” — not in the sense that it could be controlled

but in the sense that it could be subjected to calculation and converted into knowable, manageable

risks. While gamblers certainly use statistics in online poker, for them uncertainty is less a liability

to reduce, mitigate, or control than it is a resource to invite, play with, and exploit, following the

distinction made in 1921 by Frank H. Knight (2006 [1921]) between risk and uncertainty. Recently,

scholars have explored the generative, untamed aspects of uncertainty in domains such as nancial

derivatives (Appadurai 2011, 2012), day- trading (Martin 2002), and futures trading (Zaloom 2006).

Abiding Chance

567

striving for equipoise and indifference- to- outcome that gure so prominently in

the practices of poker players. What these practices share with the prayers, ritu-

als, and codes of conduct employed by ascetics, monks, and stoics is the struggle

to perform as an acting self in a eld of contingencies, uncertain outcomes, and

laws beyond human grasp. Considering them together makes salient not only the

continuities but also the discontinuities — technical and subjective — that digital

media introduce to this existential and ethical struggle; it also makes salient the

particular form that the project of chance abiding takes in the heavily nancial-

ized landscape of twenty- rst- century America.

I have chosen to focus on the experience of three gamblers, each differently

located in this landscape: Justin, a successful online poker professional; Emil, a

day trader who plays to relax; and Winslow, a graduate student in engineering who

nds the game analytically fascinating.

What they have in common is a driving

desire to heighten their capacities to abide volatility and cope with erratic down-

turns (in life, labor, and love) in the near term.

The Rise of Online Gambling

The rst real- money online poker game was dealt on New Year’s Day in 1998; ten

years later, annual revenue from online poker had grown to $6 billion. Despite

heavy legal restrictions on the activity in the United States, more Americans play

than any other national group: some 10 million in 2010 (Skolnik 2011: 117).

the close of 2011, the US Department of Justice reversed its stance on the legality

of Internet gambling, permitting individual states to institute online gambling.

Since then the gambling industry has quickly mobilized, with Nevada, New Jer-

sey, and Delaware in the lead. Restrictions on online gambling are likely to be

further rolled back as all levels of government look for new consumer activities to

regulate and tax (see Skolnick 2011; Schüll 2012).

Online poker sites commonly offer Texas Hold’em (the most popular), Omaha,

seven- card stud, and other popular versions of the game. Since the game of poker

4. I have used pseudonyms for all three online poker players. I conducted and recorded an in-

depth interview with Justin in May 2013, at an international conference in Amsterdam, and with

Winslow in July 2013 in Boston; my graduate student research assistant, Lauren Kapsalakis, con-

ducted, recorded, and transcribed the interview with Emil, also in July 2013 in Boston.

5. The 2006 Unlawful Internet Gambling Enforcement Act (UIGEA) criminalized the transfer

of funds from nancial institutions to online gambling sites, making banks largely responsible for

preventing their American clients from gambling. The law, however, did not make it illegal — or

impossible — for Americans to place bets online; nor did it take full effect until 2010, by which point

anti- UIGEA legislators were making headway with their agenda.

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6. To view a video of multitabling, see Thurman 2011. Poker sites offer tips on how to arrange the

tables on one’s screen for optimal play: “If you play only a small number of tables simultaneously, it

makes sense to arrange them in a tiled fashion all next to each other so that you can follow the action

at all tables. If you multitable eight, twelve or even more tables, you should switch to a ‘cascading’

or ‘stacked’ table arrangement” (TournamentTerminator.com 2013). Most professional multitablers

invest in a second monitor.

7. The term grinding in online poker has a different connotation than in online video games like

World of Warcraft or in live land- based gambling where “grind joints” are mocked as places for the

poor and unwise. Online multitablers go as far as to boast of their grinding powers, some even claim-

ing the title in their online name, for example, “grinder007.” While Edward LiPuma and Benjamin

Lee (2012) rightly point out that live poker has become morally and culturally valorized for its high

risk and volatility, online poker has valorized a low- volatility, seemingly unheroic mode of play.

pits gamblers against one another rather than against the house, the house makes

its money by collecting a “rake” (or percentage commission) on each cash game

played or from entrance fees for tournaments. Online purveyors stand to col-

lect far more rake than their land- based casino counterparts because players can

gamble at multiple tables simultaneously when online — an activity called “multi-

tabling.” Skilled players also stand to make more money when multitabling, for

instead of the twenty to thirty hands they might play in an hour of live poker, they

play as many as two thousand — a rate at which they can increase their exposure

to hands worth betting on.

In its speediest form, when players are gambling at

upwards of ten tables (and sometimes as many as thirty), play is referred to as

“grinding.”

Although grinders greatly increase their exposure to risk, they do so

in a way that reduces overall volatility. “In theory,” says Emil, a twenty- six- year-

old day trader and former recreational poker player, “the more hands you play, the

more the variance will even out.”

Phenomenologically speaking, the experience of online multitabling is signi-

cantly different from live poker — in which gamblers sit at a table and attend to

a single event stream, sometimes playing their cards but more often folding and

waiting. Online, players are “present,” virtually speaking, at many tables at once,

their attention distributed across a vast portfolio of games and events; there is no

waiting, just constant action. Given the quickened pace of play, the time they can

devote to each game decision is reduced. Monetary stakes, like time and attention,

are spread across multiple games, thinning a sense of investment in the unfolding

action narrative of any one table. Winnings, too, are diluted — for while prots

go up overall when multitabling, “with each additional table that you play, your

winnings per table will drop,” a poker website explains (TournamentTerminator.

com 2013). This is due to missing turns at one table while taking action at another

or to bad decisions made in haste. To optimize returns, multitablers must deter-

Abiding Chance

569

mine the maximum number of tables at which they can play well enough. “When

you’re playing in real life, you’re playing every hand the best you can,” comments

Winslow, a theoretical computer scientist and specialist in algorithmic problem

solving currently working toward his doctorate at the Massachusetts Institute of

Technology. “Online, you’re weighing optimal play per hand against the optimal

number of hands you can play in time.”

In all these respects — temporal, attentional, nancial — online poker would

appear to be a “shallow” rather than a “deep” form of play, in contradistinction to

the anthropologist Clifford Geertz’s (1973) famous description of gambling as a

profoundly meaningful encounter between subjects in which players’ social sta-

tus and very existence is at stake.

Erving Goffman’s (1967) sociological account

similarly depicted gambling as a focused, existentially freighted affair in which

card- playing heroes engaged in “character contests” that allowed them to dem-

onstrate courage, integrity, and composure in the face of contingency. By con-

trast, online multitablers who methodically click their way through thousands of

hands per session while consulting statistical indexes to guide their actions are

decidedly unheroic gures. Like other anticharismatic gures of contemporary

nance — online day traders (Martin 2002), the so- called gold farmers of video

games (Dibbell 2007), the clickworkers of Mechanical Turk (Irani 2013) — the

multitabler measures success not in the form of sudden, singular windfalls but,

rather, as an after- the- fact sum total of tiny increments.

Yet no matter how multiple the tables, how micro the stakes, and how eet-

ing each moment of play, online players cannot avoid the linear temporality of

human decision making: they must, ultimately, act from a single position in time

without knowing what the outcome will be; uncertainty cannot, in the moment of

action, be circumvented. Luhmann (1993) denes risk as the problem of making

decisions at the limit of knowledge, on the border between present and future.

Risk, adds Randy Martin (2002: 106), “presents not only the limit to what can be

known in the present, but also the burden of acting as if one could know.” Poker

tracking software and its evolving array of features and functions alleviate this

burden by enabling players to act condently yet without pretending to know what

will happen next; it provides them with “a sort of sixth sense, a datasense” (Kang

and Cuff 2005: 110) that helps to make up the epistemological shortfall of human

cognition. It equips them, that is, to better abide uncertainty — and, potentially, to

prot from it.

8. The concept of “deep play” was rst elaborated by Jeremy Bentham to describe play in which

nancial stakes run “irrationally” high despite the fact that chance will determine the outcome

(Geertz 1973: 431).

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Poker Technics

PokerTracker, Hold’em Manager, and other online tracking software depend on

the continuous tracking and recording of play- by- play game information, as it

transpires: what cards the player was holding, what plays he or she made, what

plays his or her opponents made, and, if the information gets revealed, what cards

they were holding.

These data are collected from the “chat log” that appears

below every table. Putatively there to give other-

wise anonymous players a space to socialize as

they might during live play, the log also automati-

cally records all game events as they occur (see

g. 1). Tracking software draws this information

into a database of “hand histories” that becomes

the raw material for the analytic tools alluded to

in the opening scene. In what follows I examine

these, moving from in- game tools designed to

facilitate rapid decision making to retrospective

tools designed to prepare players for future ses-

sions. The aim is to help gamblers act, in their encounters with chance, from the

vantage of an innite temporal eld in which probabilistic values can be trusted

to bear out.

Acting in Real Time: The Heads- Up Display

During a game session, the heads- up display (HUD) is the most important poker

software feature at a player’s disposal.

The HUD continuously queries a player’s

database to provide up- to- date information on opponents’ behavioral patterns,

presented in panels of letters and numbers that hover over the players’ names (see

g. 2). The gures on display, which may shift as real- time actions and events are

fed into the database of hand histories, can be read as virtual “tells”; instead of

looking at one’s opponents across the table and trying to sense them out in real

time from behind sunglasses as in live poker, an online player consults the HUD’s

9. PokerTracker, originally developed in 2001 and today in its fourth iteration, is credited with

bringing information technology solutions to online poker. Today Hold’em Manager is the leading

poker software product. For a detailed history of the evolution of poker software, see “Stranger than

Fiction” 2009.

10. HUDs are a common feature of other online gaming interfaces such as World of Warcraft,

in which they hover over other players’ avatars, communicating information about their status, their

strengths, their historical record, and the like (e.g., see Galloway 2012).

Figure 1 Play- by- play event data in an online poker chat log

(created by author)

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571

summary of historical data with a quick glance. “If I see that a player typically

never raises after he checks and is deviating from that behavior,” explains Justin,

“I can make certain deductions about how strong his cards might be.”

“You can create proles of people in a way you could never do off- line,” says

Emil. “In live poker you have to sit and watch and try to remember what a person

does to get a sense of how they play; you have to keep track of everything in your

head. Online, you don’t have to waste your energy remembering things — you

have all these statistics overlaid on the screen.” Justin comments: “I don’t know of

anyone who can actually remember this player has been at the table for exactly

eighty- seven hands and has raised preop [before any communal cards are dealt]

exactly eleven times; it’s more intuitive, like this player has been raising a lot in

the last few hours.” When betting at multiple tables online, memory becomes even

less reliable than in live poker, and intuition less available. The software works as

an “external memory,” as Justin puts it. “You trust the information more than your

own memory, and you feel more comfortable taking action and doing it faster,”

says Emil. “The numbers make the whole decision- making process easier, less

agonizing . . . it becomes much more of a binary yes- no process.”

HUD numbers may help players feel more condent in their decision- making

process, yet they do not pretend to pin down opponents’ behavior or predict what

they will do next; they do not, in other words, eliminate uncertainty. Rather, they

draw on continuously accruing historical information to gauge emergent behav-

ioral tendencies; they serve as a means for what Luhmann (1998: 69 – 70) calls

“provisional foresight,” allowing actors to adjust their responses to real- time con-

ditions. “The numbers in the display tell you, This player has certain tendencies,”

says Winslow, “and you can take that information into account right before you

make a decision about a hand.” HUD numbers amount to a kind of statistical

divination in which game data (rather than a supernatural agency) are queried for

the insights they might offer into a situation at hand. We might also think of the

HUD as a digital seer that functions as a conduit not to God but to knowledge and

temporalities beyond the grasp of human cognition and consciousness, granting

players “indirect human access” to this realm so that it might inform their future

actions (Hansen 2014: 30).

Like the stock market index, the informational scrim

of the HUD holds the status of an interpretation rather than a truth, speculation

11. In Feed- Forward, Mark Hansen (2014) argues that twenty- rst- century microcomputational

media give humans access to knowledge that would otherwise exceed their grasp, such that they may

“feed it forward” into future actions. In this way humans act as supervisors or modulators of action

rather than as transcendent agents, on the one hand, or robots, on the other.

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rather than prediction; online poker players consult its screen to modulate their

actions rather than to robotically follow its dictates.

The latest versions of poker tracking software allow players to customize their

HUD windows to show whatever mix of behavioral statistics they wish. Always

included up front, however, is a set of numbers thought to capture the core style of

any player, as measured by the percentage of hands an opponent chooses to play

(voluntary put in pot, or VPIP); the frequency of his or her betting during the rst

of four rounds of a hand (preop raise, or PRF); and how likely he or she is to keep

betting during the last three rounds of a game (aggression factor, or AF), summed

up by the shorthand VPIP/PFR/AF. A consensus has formed around the optimal

ranges for this so- called holy trinity of game statistics; values that fall outside of

these ranges “imply predictability” and therefore “can be exploited by observant

players,” a poker website explains (“Hold’em Poker Statistical Jargon Explained”

2013). Such players can glance at an opponent repre-

sented as “64/29/3” or “19/14/1.7” (as in g. 2) and

instantly know whether they are up against a seasoned

professional or an inexperienced newcomer, whether

he or she is a pushover or a heavy bluffer, and where

he or she falls on the timid- to- aggressive spectrum.

The majority of players rely on a standard array

of ten to twenty statistics in their HUD displays,

swapping suggested congurations on message boards

and trying out modications in simulations before

bringing them into live play. The most dedicated of

players tinker with the software until they arrive at a personalized set of lters that

suits them. “I use over 150 stats,” says Justin. “I select whatever outputs I want to

see on the screen and lter by them.” His current display shows forty gures in a

specic order. While a player might theoretically benet from knowing how an

opponent plays along a hundred different dimensions, HUD windows showing that

many numerical values would be cognitively draining, if not unassimilable, and

would potentially overwhelm the aesthetic experience of play itself — especially

with multiple tables open on the screen.

A secondary, more granular set of statistics pops up when a player hovers his

or her mouse over any given gure in the primary HUD. “Behind every stat is

another set of stats,” says Justin. Consulting these deeper statistics takes time; it is

done strategically. As PokerNews.com (2009), a website dedicated to discussion

of emerging tools for online poker, recommends:

Figure 2 Heads- up display (HUD) for an opponent in which

the rst three numbers designate VPIP/PFR/AF and the rest

indicate statistical scores for a variety of other behavioral

tendencies (created by author)

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It can be very useful to commit one afternoon to customizing these pop-

up screens until they show the information you want them to show. Make

sure that only the helpful information per statistic is shown. Especially

when playing numerous tables it can be very important to quickly nd the

information you are looking for . . . you will need to invest some time to

optimize the pop- ups to make them more efcient and save yourself time

when having to make a decision.

To further ease the decision- making process, poker players can congure the soft-

ware to change the color of a given indicator when it passes certain statistical

ranges. Color changes not only break up the monotony of a wall of numbers but

also alert players, via intuitive visual triggers, to opponents’ exploitable behavioral

patterns as they emerge. While basic values like AF are readily legible without

color to a moderately skilled player, more complex behavioral values — especially

those composed of numerous different statistics — are hard to detect without color

even for a player of Justin’s caliber. His advanced statistical dashboard is coded

to provide him with color cues in such cases. “Certain stats are indicators of what

to do in certain situations,” explains Justin. “So if I look at the HUD and see that

they’re all green, I know I should play aggressively.”

Software developers constantly expand the orbit of potentially signicant data

that can be automatically tracked and legibly displayed in the HUD. The capacity

to take “notes” on particular scenarios or game occurrences, for instance, was

recently added to the HUD’s repertoire. Formerly, players were urged to keep

Excel spreadsheets open during a play session, record memorable moments as

they happened, and review them periodically to nd patterns. Such a system left

it up to players to decide, in real time, that something noteworthy had happened

and to take the time to note it. Automated note taking, programmed to detect and

record the incidence of prespecied behaviors or “note denitions” (such as how

many seconds an opponent takes to make a decision, which might be correlated

with blufng), releases players from this task and frees up time for more game

play. Like statistical parameters, note denitions are “entirely customizable and

there are millions upon millions of combinations,” reports an online review of a

note- taking program (Aus_pokergirl 2010). Once a note denition has been cre-

ated, that note will ash in the HUD whenever an opponent’s behavior ts the

denition in question.

It is important to reiterate that the HUD is not an actuarial instrument that

predicts outcomes and dictates player actions but a reserve of tendential indicators

that offers clues to the directions events could potentially take. Tendencies, writes

Brian Massumi (2002: 30), can be understood as “pastnesses opening directly

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onto a future”; they pertain to “the intermediate space between what has occurred

and what is about to occur,” as Limor Samimian- Darash (2013: 3) has dened

the eld of “potential uncertainty.” The HUD provides players with a compass to

navigate this eld — that is, to more quickly detect what might be happening in any

given moment and where they might gain an edge. It is no surprise that they spend

so much time calibrating, recalibrating, and tuning this instrument of detection. “I

put quite a lot of effort into conguring how I use the software, knowing what data

to use and to combine and what you can extract from it,” says Justin.

In terms of what one can extract from the HUD, the technology is an informa-

tional guide not simply to opponents’ tendencies but to one’s own tendencies — for

in addition to statistically sussing out other players, the HUD can show a player

how he or she appears to others. “It’s also important to keep an eye on your own

stats, as tracking software has become so popular that it’s likely other winning

players at your table will be using it and looking to exploit you in the same way,”

an online tutorial suggests to novices (PokerPlayer.com 2010). Justin notes: “You

never know for sure if they are tracking you, so before assuming that, I try to

gauge what information they might have on me. I do this by looking at their

behavior toward me and also at the speed of their play against me. Based on

that, I can guess how aware they are of how I typically behave and can adjust my

behavior accordingly.” Although online poker removes the palpable social and

self- performative aspect of live play, both persist in digitally mediated fashion; the

technology provides a window through which gamblers may evaluate the actions

of others and also evaluate and adjust their own behavior. (While in the Buddhist

tradition it is by seeing one’s own actions from the same detached perspective as

others’ that one attains a state of compassionate detachment, here it affords gam-

blers a strategic, competitive foothold.)

One way Justin adjusts his behavior is to frequently change his play style when

playing against the same opponent — for example, he might alternately loosen and

tighten his range of starting hands. He thus uses HUD technology both to com-

pose a statistical prole of his opponents that can help him decide how to act in

relation to them and to gure out what kind of prole they might be composing of

him and how he might scramble the data he generates to keep them guessing about

his play style. The best prole to have is one that gives off no signals or “tells” that

might be exploited by discerning opponents (and their algorithms). Another way

players adjust their behavior to eliminate the communication of behavioral tenden-

cies is to always take the exact same number of seconds to make a decision, even

if they know immediately what they will do; some players are known to always let

their timer run down to one second before taking an action. A completely neutral

Abiding Chance

575

prole is ideal precisely because it remains in the sphere of uncertainty. Thus,

while the HUD can be a tool of uncertainty reduction when used to gauge the

potential behavior of others, when used reexively it becomes a tool of uncertainty

cultivation: the key is to methodically extinguish all signs of passion — desire,

fear, weakness — from one’s data stream, in order to seem as purely unpredictable

and uninterested as possible.

Retrospection: Postsession Analytics

While the HUD dashboard helps players dial down their passions in the heat of

the game, a different set of poker software tools helps them prepare for dispas-

sionate play through retrospective exercises. In between games, when players are

not caught up in the rapid- re stream of decisions that online poker demands,

they are invited to turn to their hand- history database and attempt to discern what

patterns and habits might be revealed there. A range of queries can be put to the

data: Am I overvaluing or badly playing certain hand combinations? Am I play-

ing too many hands from a certain position? Do I become aggressive or timid in

certain situations? To the extent that poker software encourages players to reect

upon their past action so as to shore up “leaks” in their game and better comport

themselves in future play, it can be understood as a device for self- examination,

self- discipline, and self- fashioning.

One way players can perform this self- work is through a post hoc analysis in

which they revisit the game scenarios they suspect they played suboptimally —

perhaps all hands in which they held an ace or in which they were the rst to

act — and “replay” them in the form of simulations that show “how they could

have gone differently,” as Winslow puts it. By keeping the known information

constant (i.e., the cards in one’s hand and those shown on the table) while varying

the unknown information (i.e., the cards held by one’s opponents), explains Justin,

“you can logically try to reason out the other lines you could have taken, see what

you would have won on the preop, and on the op, and on the turn and on the

river [different stages in a round of betting] — what the chances of winning would

have been if you had made any number of different choices.”

Giving an inverse twist to the Stoical practice in which individuals, in moments

of quiet before or after acting, meditated on imagined challenges so that they

could think through all the different ways they might act and thus prepare them-

selves for actual situations (Foucault 1997: 239), retrospective poker- hand simu-

lations convert actual events back into a virtual eld of potential actualities. By

returning players to a point in the past and confronting them with the branching

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576

diversity of outcomes that might have emerged from it, the simulations prepare

them to more easily “see through” the singularity of any given decision moment

and recognize the multiple futures it carries. “In the moment, the right decision

is not clear,” says Emil, “but in the aggregate, you can see how it makes sense

to act; certain things come up over and over again and start to make sense.” The

Bayesian vantage he gains through simulations lightens the consequential load of

individual game decisions and facilitates the decisive, speedy ow of multitabling.

The subjective stance sought here is one of equanimity in the face of uncertainty

and outcome variance.

Another postsession digital tool that helps players foster such a stance is the

all- in expected value (AIEV) calculator. Looking back on a session, the calcula-

tor assesses the odds a player had of winning those hands in which he or she went

“all in” against another player. (While all- in bets are relatively rare in live play,

they occur often in online multitabling due to the sheer volume of hands players

encounter.) “I can look back and say, Today I got into ten 50/50s and ve 20/80s

and four 40/60s and six 70/30s,” reports Winslow. In other words, he made ten

all- in bets with a 50 percent chance of winning, ve with a 20 percent chance, and

so on. Also called pot equity, AIEV calculates what a player theoretically “owned”

of a pot. “Basically, if you have a 40 percent chance of winning, you can think of

that in the long run as owning 40 percent of it,” Justin explains, “because if you

played the hand out an innite number of times, that’s how it would work out. So

that’s your expectation.” In actuality, a tie notwithstanding, one player will walk

away with the entire pot and the other with nothing. Thus the AIEV calculator can-

not be described as a predictive technology, even in the retroactive sense, for it is

concerned not with how a specic hand will turn out but rather with what a player

can statistically expect from it. “Your expectation is based on the long term, and

that’s what should tell you how to act in the short term,” says Emil. The point is to

base one’s expectations and one’s actions in an innite rather than a nite register.

To that end, poker players like the one described at the start of this article are

emphatically encouraged to disregard (and, indeed, to renounce) their actual all- in

winnings — for they might have won every all- in wager they made during a session

of play, but only out of luck. Instead of calling up winnings after a session of play,

they should call up their AIEV scores. Furthermore, they are advised to do so only

after a statistically signicant number of sessions have been played, since only a

large number can be trusted to render an honest assessment of their performance.

“Once we have played enough hands to make our sample size meaningful, the

data will be more honest than our own impressions of how we stack up,” writes

one player on his personal poker blog (Chris 2011; emphasis added). If players

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577

nd their scores to be in the negative range, they know they have been playing too

loose (e.g., betting on too many 20/80s and not enough 80/20s); if they nd their

scores favorable, then they should feel good about their performance — regardless

of actual game outcomes. “If you’re playing well,” says Emil, “you should feel

just as good whether you’re losing or winning.”

Financial traders, Caitlin Zaloom

(2006: 128) reports, are similarly invested in “dismantling narratives of success

or failure.” She describes how managers at one trading rm claimed that they did

not care if traders made or lost money as long as they practiced discipline: “The

trader’s responsibility was to his technique of self- regulation, not to the prot and

loss gure at the end of the day” (ibid.: 129).

As in nancial trading and other activities involving short- term volatility, to

play online poker well one must strive to renounce the pleasure of immediate

worldly gains; pleasure should come not from the outcome of a given moment but

from consistently following the rules of practice — in this case, the rule of large

numbers. “What you care about is the long haul, and you learn to rise above the

moment,” says Winslow. “In practice, when I play it’s just a rationality exercise

where I enjoy the feeling of making a bunch of correct decisions throughout the

day.” Justin describes his own renunciation of game outcomes:

I never look at what I won; I just rate my play performance. I don’t care

how much money I made — it’s totally irrelevant, there’s almost no value to

it. . . . I guess knowing that might inuence my happiness in the moment,

but that itself is ridiculous since I should be happy or not based on how

well I played. I want that to be an emotional trigger; I don’t want any emo-

tions connected with using or winning money, because it’s totally useless.

Some days I win, some days I lose.

While a losing player in a live game of poker might take small comfort in the

knowledge that he or she “played correctly” (i.e., according to statistical laws),

in the context of online multitabling where he or she plays tens of thousands of

hands every month, such knowledge grants a sense of ontological security. The

ontology at stake is that not of a self whose value is determined in moments of

winning or losing but, rather, of a self whose value accretes through many tiny

actions over time.

To optimize his or her value potential, such a self must respect

the law of large numbers at every decision point.

In keeping with this respect, skilled online players resist the temptation to ret-

rospectively query or consult their tracked data too frequently. Winslow explains:

12. Elsewhere I discuss how users of tracking technology come to regard themselves as “time-

series selves” (Schüll 2016).

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578

“A lot of novice players get impatient and make the mistake of overvaluing their

data — they get biased by short- term information and ultimately make poorer deci-

sions. You have to have a lot of data points for anything you detect to be statisti-

cally signicant — otherwise you can’t condently conclude that a pattern is real.”

He depicts himself as a dynamic database whose “real” value is emergent and

impossible to assess without sufcient temporal resolution. Justin echoes his point:

“It’s important not to look at the data too often, because you need to have a fairly

large number of hands not to be fooled by randomness. You have to safeguard

yourself against that.”

Another safeguard against the overvaluing of online poker data is the practice

of sharing that data — one’s HUD congurations, hand histories and simulations,

AIEV graphs, and the like — with other players. Evoking the face- to- face conver-

sations and epistolary correspondence that played such a critical role in the ethi-

cal self- work of Greco- Roman citizens, online- poker peers engage each other in

collective dialogue via Internet forums, chat threads, and message boards. “Share

your experiences with those who can relate,” reads a post to an online forum.

“After I review my hand histories and I am not able to nd my mistake, or I did,

but nd myself struggling with a particular bet, call, street, etc, I talk it over with

several other poker players and see if a consensus appears. I also post my hand

for review. Doing this has been extremely helpful to my game and in identifying

leaks. Be prepared though — the truth sometimes hurts” (StormRaven 2009). Plac-

ing oneself under the gaze of others, Foucault (1997: 221) observes, is “a matter

of bringing into congruence the gaze of the other and that gaze which one aims

at oneself when one measures one’s everyday actions according to the rules of a

technique of living.” Like the Stoics, for whom offering commentary on others’

self- reports was as important as receiving it, online poker players do not simply

upload and solicit feedback on their own data but also evaluate and respond to the

data that others post. “The opinions that one gives to others in a pressing situa-

tion,” notes Foucault, “are a way of preparing oneself for a similar eventuality”

(ibid.). Although digital media may dampen the social dimension of game play as

well as players’ sense of self, between game sessions it affords a space for com-

munal exchange and self- fashioning.

Combating Tilt

Each of the software tools I have considered thus far, whether in- game or retro-

spective, individual or communal, is designed to help online poker players ward

off the dreaded state of tilt and attain dispassionate conduct in the face of chance.

Abiding Chance

579

The challenge they face — to act in worldly time without being affected by event

outcomes — is akin to the challenge that online nancial traders face as they move

in and out of trades in a matter of seconds, striving all the while to “treat each

trade as if it has no effect on the next” and to “ignore a sense of continuity”

between past, present, and future trades (Zaloom 2006: 133 – 34; see also Knorr-

Cetina and Bruegger 2000, 2002; Zwick 2005, 2012).

Some gamblers use software designed specically to protect against tilt — like

Tilt Breaker, which features take- a- break reminders; “automated lockdowns” trig-

gered by big wins, a certain number of hands played, or a certain amount of time

played; and a Rage Quit button for moments of “super tilt.” Other gamblers prefer

to focus on self- discipline — “preparing and disposing [the] soul to rid itself of all

its disordered affections,” as the Jesuits characterize the spiritual battle they face

(quoted in Quattrone 2015: 423). As poker’s “law of least tilt” dictates, “between

two players of equal skill, the player with the most discipline will prevail over the

long run” (Forte 2015: 136).

In a poker- forum thread titled “The Many Faces of Poker Tilt,” one mem-

ber composed a long post advising his peers on how they might track, manage,

and ultimately avoid tilt (SitandGoPlanet.com 2011). He began by distinguishing

between the main forms of tilt: angry tilt, in which losses despite statistically cor-

rect play tip players into overly loose and aggressive play; frustrated tilt, in which

mounting exasperation at being dealt bad cards and having to fold for long periods

triggers impulsive, sloppy play in games that players should exit; fearful tilt, in

which the trauma of past losses results in overly tight and passive play; and, nally,

despondent tilt, in which others’ luck leaves players feeling they are bound to lose,

a form of resignation that negatively affects their play and threatens to become a

self- fullling prophecy. “Beware of your really ‘giddy or euphoric’ feelings too!”

warned another poster (Maid Marian 2009); “the strong emotions aroused by

winning can be just as mind- clouding as any form of poker despair,” echoed the

author of another online post on managing tilt (Connors 2013). The point is to

practice indifference to events as they unfold, calling to mind the aims of Eastern

meditation and echoing the highest virtue put forth in the Jesuits’ Spiritual Exer-

cises: “I ought to nd myself indifferent . . . to such an extent that I am not more

inclined or emotionally disposed toward taking the matter proposed rather than

letting go of it” (quoted in Quattrone 2015: 428).

The author of “The Many Faces of Poker Tilt” went on to urge his fellow players

to “set up a tilt management plan” with ready- at- hand techniques for identifying

and combating tilt in its various guises (“Poker Tilt — The Many Faces of Poker

Tilt” 2011). He recommended that they perform “self- checks” every thirty minutes

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580

by taking inventory of any feelings of frustration, revenge, anger, or despondency

that might be creeping into their game, rate the severity of those feelings, and

apply counteractive measures. One might “walk away from the computer immedi-

ately,” for instance, and stay away for ten minutes, if sufcient to “un- tilt” oneself,

or for twenty- four hours, if necessary; the important thing is to “ensure that you

stay away long enough to rationalize the cause(s) of your tilt” (ibid.).

The work of rationalizing the causes of a tilt episode could involve “spending

some time re- tooling your game” (ibid.) by way of retrospective investigation

(“I recommend reviewing hands after each session, unless you are on tilt or too

tired — then save it for the next day” [StormRaven 2009]); self- education on blogs

or from poker- strategy books and websites (“thou shalt understand probability

and variance” reads an article on rules to avoid tilt [“How to Avoid Tilt” 2010]);

or posting data from one’s tilted session on poker forums and message boards so

as to receive feedback and advice. In one online discussion, a gambler describes

how he writes down every “automatic negative thought” that crosses his mind

during play and afterward writes out a “rational response” to each in an effort to

banish them from future play (negtv capability 2001). His method recalls the Stoic

practice of constantly screening thoughts to approve or disprove them, to verify

and ensure that one remains in control; it likewise recalls the early Christian

practice of writing down thoughts and actions as a safeguard against sinning. In

the secularized context of online poker, gamblers understand themselves to be at

a similar moral crossroads: instead of being pulled between God and Lucifer they

are pulled between rational indifference and the passion of tilt.

Justin has developed a particularly elaborate system of self- regulation to man-

age his reactions to in- game events and protect himself against tilt. Directly before

a session he consults his “warm- up checklist” (see g. 3), a document he regularly

revises. Simple items — such as making sure that his desk is clutter- free and that he

has a glass of water and has eaten enough food to sustain him through a session of

play — are accompanied by larger goals, notes on how to raise motivation (e.g., do

some push- ups or study poker), and categories such as “mental focus points.” This

last item includes the only entry he has underlined: “Take the time for decisions.

Count out loud.” Directly beneath this line is a sublist of “reasons to take time

before clicking / making a decision,” the rst of which reads: “I click less from

emotion.” Justin reects:

You’re making so many decisions that a lot of them will just happen

intuitively. In most cases that’s ne, but when I enter that gray area where

it’s not certain what I should do, I want to make sure I don’t rely only on

Abiding Chance

581

my own intuitions. What I do is pause every time I’m facing a difcult

decision. I try to count down in my head, three, two, one . . . I breathe in

and out and try to override my intuition. Recently, I ordered a metronome

to see if it might help with that process and prevent me from making deci-

sions too quickly. My thinking is that if I have a metronome, it will give

me some sort of external rhythm. I plan to experiment with that.

While the HUD serves as an “external memory” for Justin, a metronome, he

hopes, could function as an “external rhythm” to bring him out of the affective

intensity of uncertain moments and restore him to the realm of rational reec-

tion, presence, and equanimity. His plan to deploy the metronome as a tool for

self- modulation brings to mind the use of chants, breathing exercises, body rock-

Figure 3 Justin’s “warm- up” checklist (anonymously shared with author)

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582

ing, and other rhythmical techniques used in meditation and prayer. The Jesuits’

Spiritual Exercises, for instance, explicitly directs subjects to pray “according

to rhythmic measures” (quoted in Quattrone 2014: 426). As Ignatius writes, “I

should nd myself in the middle, like the pointer of a balance” (quoted in Quat-

trone 2004: 660).

After every session of poker, Justin assesses his equilibrium performance by

means of a “cooldown checklist” (see g. 4), recording the time of day he played

(morning, midday, or evening), the amount of time elapsed, the total number of

hands played, and scores for focus and technique based on customized rating

criteria that range from “mega- tilted” to “maximal game- time spent focused.”

Finally, he records comments on areas for self- improvement. One entry reads:

“Evening, 120 minutes, 1,305 hands played, Focus 7, Technique 7. Think it went

ok. Next time: better focus, tighter play, fold preop when in doubt.” “I use the

information to try to adjust my behavior in the next session,” he says. “I have a

whole working document with a long list of things I could adjust. I am constantly

revising it.”

Justin’s tilt- assessment checklists are not unlike the self- scrutinizing, self-

doubting diaries of the Puritans, in which they took rigorous inventory of their

passions in an effort to renounce them. The checklists bear a particular resem-

blance to the moral accounting system of Benjamin Franklin (2003 [1791]), which

bound him to the daily marking up of an ivory slate containing thirteen rows of

virtues and columns for each day of the week (see also Paden 1988; Weber 1958

Figure 4 Justin’s “cooldown” checklist (anonymously shared with author)

Abiding Chance

583

[1905]).

In an earlier system of similar design, Jesuits kept records of their daily

sins:

For each sin committed from the moment of rising until the rst exami-

nation, the exercitant was required to enter a dot on the upper line of the

rst [day]. This step was followed by “one’s resolution to do better during

the time until the second examination,” which was made that night after

supper. At that time other dots were placed on the lower line of the [day]

and the gure examined to see if the situation had improved or worsened

over the course of the day. This examination was to be repeated each day

of the week. (Quattrone 2004: 657)

The routinized record keeping of sin served to allow monks “to remain indiffer-

ent and unfettered while formally disciplined,” writes Paolo Quattrone (2014: 36).

The point was to subject themselves to a methodical regimen of self- accounting to

identify and weed out passion, desire, and attachment, facilitating equipoise and

rational conduct. The ethical subject gured here — as in digitally facilitated online

poker — is one whose actions, in a eld of uncertainty, derive from internal equanimity.

Lessons for Life

Software- assisted online poker and its technological mediations, I argued above,

help gamblers develop a subjective readiness for their encounters with chance

within the game. As they see it, the readiness- toward- chance that they practice

online carries over to life off- line. Winslow reects:

You’re tougher when things don’t go your way in life because you’re used

to making the right decisions and not having things go your way in poker.

When you play a lot online, at multiple tables, you can very visibly see

the swings — you learn that in the short term there will be lots of variance,

even if you’re making all the right decisions. You get a very good sense of

13. Writes Franklin (2003 [1791]: 152): “I should have, (I hoped) the encouraging Pleasure of

seeing on my Pages the Progress I made in Virtue, by clearing successively my Lines of their Spots,

till in the end by a Number of Courses, I should be happy in viewing a clean Book after a thirteen

Weeks daily Examination.”

14. As this passage attests, the division of the self into minute bits whose value can be “added

up” is not specic to the digital era. Paolo Quattrone (2014: 26), probing the links between Jesuit

spiritual exercises and their methods of record keeping and nancial accounting, nds that both fol-

lowed a logic in which “an entity, be it the self of the Jesuit member or the wealth of the college, could

be divided into its smallest constituent parts via detailed analytical schema and then aggregated up

into a description of the whole.” Accounting, he concluded, is a method “that begins with making an

inventory, be it of the self or of assets and liabilities, and ends with salvation” (ibid.: 27).

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the degree to which luck is at work, how much it matters. And you realize

that it’s no different in life: sometimes you do the interview very well and

you still don’t get the job. Thinking this way helps you stop connecting

particular outcomes to your performance. This type of mentality really

helps me when I fail at something in life and, by the same token, when

I succeed — because even if you win, it could have been due to luck, not

because you made the optimal decision at every turn. You can kind of see

through a bad or a good outcome to all the other ways it could have gone.

Life events, the game of poker trains its players to see, are meaningful only as

part of a pattern, and that pattern is revealed only over time. As Puritans live

under the mercy of a God whose will cannot be discerned or inuenced, poker

players lives under the mercy of chance; their only recourse is to abide short- term

variance and place their faith in the long game; divine providence is replaced by

the providence of probability.

The analogy comes across in a quotation from a software developer who

designs programs to help players resist the tendency to become tilted by the “injus-

tice of the game.” In his blog post “How to Avoid Tilt” appears rule 9, titled “The

Poker Gods Knoweth No Justice”: “There really is no justice to this game, at

least not until the very, very long run of things, but it’s really just a microcosm

of life isn’t it? You will have horrible, gut- wrenching downswings where noth-

ing goes right and nothing is fair; but you must persevere” (“How to Avoid Tilt”

2010; emphasis added). As followers of Calvin had no way to intercede in God’s

decisions about who would be saved and could only be humble, self- vigilant, and

methodical in their daily dealings, poker players have no way to inuence chance

and can only play as much, as fast, and as well as they can. The post’s injunction to

“persevere” recalls the comportment protocol of the Puritan, who engages in what

Arjun Appadurai (2015: 6) characterizes as “a continuous wagering of oneself in

the routines of methodical moneymaking” — not to assure salvation but as a sign

of faith despite radical uncertainty.

15. Contemporary nancial actors, writes Appadurai (2011: 524), use “intuitions, experiences,

and sense of the moment to outplay other players who might be excessively dominated by their tools

for handling risk alone.” He explains: “We might say that while some actors in the eld of nance

do know what they don’t know, and perhaps also what they would like to know, they certainly have

no good way to measure what they don’t know, and even more, they do not know how to measure

it probabilistically. Thus uncertainty remains outside all nancial devices and models” (ibid.). An

important reference for Appadurai is the classic work of Knight (2006 [1921]) on uncertainty, which

went against the grain of economic thinking by arguing that prot can arise from absolute unpredict-

ability, not only sober methodicality.

Abiding Chance

585

And yet a small but important difference in the attitude and mode of subjec-

tion of the online gambler is revealed in the very next line of the post: “Create

your own justice; continuously push forward until the numbers inevitably yield

in your favor” (“How to Avoid Tilt” 2010). On the one hand, to “create one’s own

justice” is similar to Puritan perseverance: it is not a recipe for mastering chance

but, instead, involves mastering an attitude of indifference to the outcomes chance

deals, so that one can act more gracefully in relation to it. On the other hand, the

post promises that acting in this way will yield prots in the end. “In the long

run,” explains Winslow, “if you make right decisions — the statistically correct

decisions — you’re likely to reach your optimal statistical potential and come out

ahead.” While this form of statistical salvation is not assured, a certain promise

attaches to “correct decisions,” as for Christian ascetics and Jesuits alike, whose

exercises, writes Quattrone (2015: 427), “began with the realization of being in

perdition and eventually ended with the possibility of making the right choice, of

nding salvation and realizing a vision of truth” (emphasis mine). The perdition of

poker players is that of nonstatistical thinking, and the vision of truth that becomes

manifest in their databases and self- accounting logs is that not of God but of prob-

ability. “It’s one long session,” writes a poker blogger (Connors 2013); “We are

taught to focus on the quality of our decisions, and if we make enough of them,

we will win in the long run,” writes another (Tag 2011). Online poker software

enhances “the quality of decisions” that gamblers make by helping them come

to trust that variance will yield to smooth gains in time, as long as they cultivate

indifference to outcome, tend to leaks in their game, and “persevere.”

At the same time, they worry that indifference, taken to its logical extreme,

might squeeze out the possibility for decision making altogether. “If everyone uses

these stats and uses them correctly,” says Emil, “then there will be no room left

to have an edge — because everyone will have the same information, like we’re all

bots playing each other.” When a point is reached where no uncertainty remains,

he goes on, “the game will be ruined for everyone.” Online gamblers’ anxiety

over the increasing automation of the game is most obvious in the shunning of

poker bots — algorithms that pose as players, multitabling around the clock to

collect vast quantities of data on real players that others can later purchase to

access detailed informational proles on opponents they have never before played

against. This is considered “cheating” in no uncertain terms — a shameful viola-

tion of the rules of the game. Yet alongside easy denouncements of poker bots is a

creeping concern among players that their own use of poker tracking tools, now a

universally accepted aspect of online play, might be turning them, for all intents

and purposes, into robots.

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The Jesuits had a similar concern, recognizing that their spiritual exercises

“needed to be channeled toward the possibility of a choice and therefore toward

action” but also needed to avoid being so prescriptive that they led to “unreective

action, depriving a Jesuit member of that indifference that characterized the very

essence of being a Jesuit and made him able to exert wise judgment” (Quattrone

2014: 29). In other words, they recognized that to act indifferently one needed,

rst, to have difference — a measure of uncertainty between choices. When digi-

tal technology is mobilized in the service of worldly action, as we have seen in

online poker, this kind of difference is more quickly and thoroughly squeezed out;

online poker players are drawn into streams of action so fast and hypermediated

that their status as ethical subjects wavers. The equipoise they seek in decision

making comes rather too automatically and perfectly, prompting the question:

How not to be a bot?

Justin’s response to this question was to make a small but signicant revision

to his poker regimen, as described below. The revision, prompted not by a lack

of success in poker but by the failure of a romantic relationship that had seemed

to him a “perfect match” on paper, was based on his realization that to act both

optimally and humanly in moments of uncertainty, he must not allow himself

to become fully robotic but rather must leave himself open — just a tiny bit — to

signals of an affective, qualitative, intuitive nature. He explains how this new

orientation departs from his former discounting of all emotion as illusory and in

need of taming:

If you imagine a scale from – 5 to +5, I would say that I want to be at a +1.

For a very long time I thought the best state to be in was zero — I operated

that way for years. Operating at zero, you’re acting like a perfect robot.

But the risk in that for me was that I almost didn’t listen to any emotional

signals, because I was trying to rationalize everything. But now I try to let

in a signal so I can then decide if I should take that signal into account in

my decision- making process or not.

To get himself into the target state of +1, Justin takes simple measures: “One of

the things in my warm- up used to be not drinking coffee — but now I always drink

one cup of coffee or espresso before a session; it has become a ritual.” Music is

also important: “Basically what I do is congure my playlist to get me in that

emotional state of +1 — so some days I choose mellow music, because maybe I’m

already at a 3 and I need to bring myself down, and other days I choose more acti-

vating music to bring myself up.” Justin’s advanced experiments in quantied self-

regulation have led him, perhaps ironically, to conclude that too tightly bracketing

Abiding Chance

587

his emotions closes him off from the potential that lies in the uncertainty of the

game and sties his ability to respond decisively to that potential. He elaborates

on his affective reorientation from zero to +1:

I’ve come to understand that if I use a rational model for everything and

become more robotic, then I feel disconnected from the world and not

really sure of what I want to do. . . . That’s why I try to open the inter-

val to +1. Before, I tried to ignore or discount my gut feeling because

I thought it was never to be trusted; I didn’t know what I could do with

it. Now I try to use it as a signal in those gray areas where things are

uncertain.

As Justin tells it, the interval of +1 marks the interval of uncertainty and poten-

tial passion that he recognizes he cannot do away with — and, indeed, should

not — in his quest to live and act in the world as an ethical subject. Diverging from

Harman’s above- quoted wish to be a robot, he creates for himself a space in which

the task is not to statistically assess or programmatically execute but to intui-

tively apprehend and exercise choice outside of algorithmic parameters.

Justin’s

revised approach, however, raises a new question: Is his new protocol a break with

a robotically rational paradigm, or not? The space of possibility that he opens is

not, after all, open- ended; instead, it is numerically bounded and associated with

a whole set of procedures. Despite his claim to move beyond reason, one might

interpret the +1 system as an even more rational way to cope with the things he

cannot know than his former zero- oriented system. In the end, his answer to the

question of how not to be a bot remains ambivalent.

Justin’s struggle to equip himself for making decisions under uncertainty takes

part in — and offers a window onto — a more general predicament of heightened

exposure to economic volatility. The circumstances of uncertainty that online

poker players face and the pressure on them to adjust to these circumstances are

not unique to the space of games or even to professions like nancial trading;

they are continuous with everyday life. As Caitlin Zaloom (2016) observes in her

16. In contemporary nancial risk taking, Appadurai (2011, 2015) discerns a dispositional turn

from the methodicality and self- doubt of Puritanism toward a heady, “swashbuckling” condence. In

his account, as market devices become hypermethodical, market actors become “avaricious, adven-

turous, exuberant, possessed, charismatic, excessive, or reckless” (Appadurai 2011: 524). Online

poker players grinding methodically through poker hands in front of their multiple screens paint a

rather less exuberant prole of contemporary market actors and also suggest that devices and actors

are more blurred than they are divided. Their mode of uncertainty — their “uncertainty imaginary,”

to use Appadurai’s term (ibid.) — is a dispositional admixture of anxious self- discipline and specula-

tive ambition (in a dose of exactly one unit, in Justin’s case) that is well captured by Pat O’Malley’s

(2000: 465) phrase “enterprising prudentialism.”

Public Culture

588

ethnographic analysis of Christian money management, believers embrace a reli-

giously inected repertoire of nancial tools and techniques to help them navigate

economic decisions “in the face of obscure forces” (2016). Evangelical budgetary

practices, she argues, illuminate the force of the ethical demand to abide volatility

(in the form of market uctuations, job insecurity, debt, credit borrowing, and the

like) that citizens of the contemporary nancial economy face.

Online poker and

its software offer a quantitatively sophisticated response to this demand, bringing

players into intimate contact with statistical innitude and its obscure laws. The

tools, techniques, and practices of self- discipline they develop to help them act

within this sphere without falling into “tilt” express the challenges and dilem-

mas of living and acting within the fast- moving, highly uncertain terrain of the

present- day economy.

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Natasha Dow Schüll is a cultural anthropologist and associate professor in New

York University’s Department of Media, Culture, and Communication. Her first

book, Addiction by Design: Machine Gambling in Las Vegas (2012), is an ethnographic

exploration of the relationship between technolog design and the experience of

addiction. Her forthcoming book Keeping Track concerns the rise of digital self- tracking

technologies and the new modes of introspection and self- governance they engender.