Why do smart people sometimes make decisions that seem to work against their own interests? What if you could predict your competitors’ moves and always stay one step ahead?<\/p>\n\n\n\n
Game theory offers insights for navigating everything from business negotiations to everyday conflicts. The Art of Strategy<\/em> by Avinash K. Dixit and Barry J. Nalebuff explains how understanding strategic thinking can transform your approach to competition and cooperation.<\/p>\n\n\n\n Keep reading for our overview of The Art of Strategy: A Game Theorist’s Guide to Success in Business and Life<\/em>.<\/p>\n\n\n\n\n\n\n\n The Art of Strategy<\/em><\/a> by Avinash K. Dixit and Barry J. Nalebuff explores how you can apply strategic principles of game theory<\/a> to your business and everyday life. The authors argue that success in any competitive situation depends on understanding your choices as well as your opponent\u2019s<\/strong>: anticipating moves, reasoning backward from your ultimate goal, and recognizing when to act against your own interests. They examine real-world case studies to explain foundational game theory concepts such as the Nash equilibrium<\/a>, dominant strategies, and how to encourage people to make selfless choices for the good of the group when selfish ones will benefit them individually.<\/p>\n\n\n\n In this overview of The Art of Strategy: A Game Theorist’s Guide to Success in Business and Life<\/em> (published in 2010), we\u2019ll explore general game strategies as well as specific strategies for the various types of games you may encounter in your everyday life.<\/p>\n\n\n\n Dixit and Nalebuff define a game as a set of interactions between people or organizations in which each player\u2019s options, decisions, and outcomes depend on the decisions of the other players<\/strong>. They call this \u201cstrategic interdependence.\u201d<\/p>\n\n\n\n As Dixit and Nalebuff explain it, your overall strategy when playing a game is to try to predict what the other players will do so you can make choices that counter, nullify, or otherwise work off their moves so that your interests<\/a> are served no matter what they do. To make these predictions, you\u2019ll consider the goals, motives, and points of view of the other players. You\u2019ll also assume they\u2019re<\/em> trying to predict your<\/em> next move as much as you\u2019re<\/em> trying to predict theirs<\/em>.<\/p>\n\n\n\n In general, when playing games, you should assume other players are motivated by self-interest<\/strong>. However, Dixit and Nalebuff note that human motivations aren\u2019t always straightforward. People are driven by a combination of selfishness, benevolence, justice, fairness, and short-term and long-term considerations. They\u2019re influenced by emotions like shame, fear, and happiness. They often act irrationally, can be well aware that their own interests can align with yours, and are likely to engage in \u201creciprocal altruism,\u201d whereby they act selflessly in the interest of a larger group. These differing motives are born of evolutionary drives that sometimes prioritize individual survival and sometimes prioritize group survival. Therefore, though people will usually<\/em> try to further their interests at the expense of yours, this isn\u2019t always the case.<\/p>\n\n\n\n Dixit and Nalebuff categorize games by two overall characteristics\u2014games are either:<\/p>\n\n\n\n We\u2019ll explore each of these distinctions below.<\/p>\n\n\n\n Dixit and Nalebuff explain that if the interests of each player directly conflict with the interests of the other players so that one person\u2019s win is another person\u2019s loss, the game is a zero-sum game<\/em>. Examples are sports championships and job applications\u2014for one team or applicant to win, all others have to lose. <\/p>\n\n\n\n Non-zero-sum games<\/em> are those in which multiple players can either benefit or lose at the same time. Examples include business transactions where buyer and seller both benefit from a deal, or where they both lose out by not agreeing to a price. <\/p>\n\n\n\n Dixit and Nalebuff write that most games of life, business, and politics are a combination of these two types of games\u2014players can either both win, both lose, or end up somewhere in the middle, where one wins because the other loses to at least some degree. Continuing the example above, this might mean that the resulting business deal benefits both parties, but benefits one more than the other.<\/p>\n\n\n\n Dixit and Nalebuff note another characteristic of games, which is that they are either played sequentially<\/em> or simultaneously<\/em>:<\/p>\n\n\n\n Dixit and Nalebuff devote the majority of their book to discussing simultaneous games, which are more complex than sequential games because you can\u2019t see the other player\u2019s moves before you choose your own moves. Our guide will do the same\u2014we\u2019ll explore sequential games briefly, then spend the rest of the guide on simultaneous games.<\/p>\n\n\n\n To successfully navigate sequential games, Dixit and Nalebuff recommend using a <\/strong>game tree<\/em><\/strong> to look forward to your ultimate goal and then reason backward.<\/strong> A game tree is a type of decision tree<\/em>, which is a common analysis tool where you write down a starting choice, then draw a line from it to each possible next choice. From those choices, you draw lines to subsequent choices, and so on. The resulting chart will resemble a tree branching out from a central trunk.<\/p>\n\n\n\n For example, if your end goal is to become a lawyer, your decision tree\u2019s trunk\u2014your starting option\u2014might be \u201cgo to college.\u201d From there, your branches could point to different colleges that offer law programs. From there, your branches might point to the specialties each college program offers, as well as potential internships each college might lead to, and so on. You could then judge which path is most likely to lead you to your desired job. <\/p>\n\n\n\n A game tree differs from a simple decision tree because it also accounts for the decisions of others.<\/strong> For example, if you\u2019re playing checkers, your choices at any point could be to move one piece here or another piece there. In either case, your opponent would then have specific choices in response, leading to subsequent options for you, and so on. To use a game tree, keep in mind your goal destination (capturing all your opponents pieces), and then choose the path that is most likely to lead you there, judging along the way your opponent\u2019s likely responses to your moves. In this way, think ahead to where you want to end up, then work backward to figure out how to get there. <\/p>\n\n\n\n Dixit and Nalebuff note that game trees lose their usefulness in extremely complex cases\u2014for example, if you\u2019re playing chess rather than checkers, where each move reveals so many choices for both players that even supercomputers struggle to analyze them. But in games you encounter in everyday life, such as business or political deals, this approach generally works\u2014pick your end goal, figure out the steps to get there taking into account your opponent\u2019s likely reactions, and choose the path most likely to succeed.<\/p>\n\n\n\n Dixit and Nalebuff devote the majority of their book to exploring simultaneous<\/em> games, where players make their moves at the same time. Because players don\u2019t have the advantage of seeing other players\u2019 choices before they get their turn to choose a move, everyone has to predict what the other player will do at the same time they\u2019re planning their own moves. <\/p>\n\n\n\n Dixit and Nalebuff write that most games of everyday life are simultaneous games<\/strong>. For example, two companies bringing similar products to market will plan their marketing campaign<\/a> without knowing how the other company plans to market their<\/em> product.<\/p>\n\n\n\n Dixit and Nalebuff write that in many games, there\u2019s a tension between selfish choices that benefit an individual and selfless choices that benefit a group as a whole. Individual actions can lead to collectively worse outcomes<\/strong>, but cooperative<\/em> success is often possible only if everyone<\/em> in a group acts against their own individual interests\u2014a difficult thing to pull off. When faced with a choice to either ensure their own survival or to risk their personal gain for the good of the group, players often act selfishly, even if they\u2019d ultimately come out better by acting in the group\u2019s interest.<\/p>\n\n\n\n A famous illustration of the way games inherently encourage selfishness, even when it ultimately doesn\u2019t benefit the players, is the prisoners\u2019 dilemma<\/em>. Dixit and Nalebuff use this game theory concept to explain the related concepts of dominant strategies<\/em>, the Nash equilibrium<\/em>, and the tragedy of the commons<\/a><\/em>. We\u2019ll explore each of these below. <\/p>\n\n\n\n The prisoner\u2019s dilemma is a classic game theory concept that demonstrates how individuals acting in their own self-interest can end up hurting their interests. In a prisoner\u2019s dilemma, two criminals are interrogated separately about a crime. Both did the crime, but both naturally want to get away with it. To convict them, the police need a confession. While they\u2019re interrogated, a prisoner won\u2019t know if the other is confessing or staying silent, so their dilemma is as follows: <\/p>\n\n\n\n Thus, they both benefit most if they both stay silent\u2014but only<\/em> if they both<\/em> stay silent. As each one stays silent individually, they each risk the worst outcome, where the other confesses to ensure a lighter punishment. Ultimately, then, they both have an incentive<\/a> to confess, even though that\u2019s the choice that leads to a somewhat bad result for everyone. <\/p>\n\n\n\n Dixit and Nalebuff note that this game dynamic shows up in real life situations when, for example, companies engage in price wars: Companies A and B will both benefit if they both keep their prices high. If Company A lowers prices, they\u2019ll gain customers (and profits) as Company B loses customers (and profits). If both<\/em> lower prices, they\u2019ll both end up with lower profits, but they\u2019re both incentivized to lower profits so that they don\u2019t lose customers to the other. Thus, they each have an incentive to make the choice that leads to a somewhat bad result for both of them.<\/p>\n\n\n\n The best approach to navigating simultaneous games is to use what game theorists call a dominant strategy<\/em>\u2014a strategy that will deliver a good outcome whether the other person chooses selfishly or selflessly.<\/p>\n\n\n\n In the prisoner\u2019s dilemma, this means confessing early, because you\u2019ll avoid a harsh punishment whether or not the other stays silent. This points to a paradox that contradicts typical economic theories espoused by free-market thinkers such as Adam Smith: When every player pursues their own self-interest, it leads to an outcome that is worse for everyone. <\/p>\n\n\n\n In a price war discussed above, the dominant strategy would be lowering your prices, even though again, this leads to lower profits for everyone. It at least guarantees your company still has some<\/em> profits, instead of losing all customers to your competitor.<\/p>\n\n\n\n When playing a simultaneous game, you can usually arrive at your best choice by figuring out the equilibrium<\/em>\u2014and specifically, the Nash Equilibrium.<\/em> Named after mathematician John Nash, this is the choice that benefits both players based on what they believe the other is most likely to choose, knowing the other player is also making the same judgments. This allows for an outcome where both players are generally happy and thus, their choices are stable<\/strong>\u2014neither has incentive to change their decision because they\u2019ve already arrived at the most beneficial outcome. In the prisoners\u2019 dilemma, the Nash equilibrium is where they both confess, knowing the other will likely also confess.<\/p>\n\n\n\n An example of how this strategy plays out in real life is in the pricing war mentioned above. Each company wants to avoid two outcomes: pricing their product too high so they lose customers to the other company, or pricing their product too low so they lose profits even if they gain customers. While they\u2019re weighing these risks, they know that the other company is weighing the same risks, and also wants to avoid the same fates. <\/p>\n\n\n\n Each company will thus likely set their price at a level that\u2019s high enough to cover their costs but low enough that they know the other company is unlikely to underprice them because they need to cover their own<\/em> costs. In this way, the two companies can settle on a stable level for their prices that removes the need for them to constantly adjust their pricing in response to the other.<\/p>\n\n\n\n Dixit and Nalebuff write that the vast majority of simultaneous games in real life are ones where both parties benefit by cooperating, not competing, and thus, most games have a Nash equilibrium\u2014a stable solution that both parties individually decide is most likely to benefit both of them based on what they think the others will likely do. <\/p>\n\n\n\n Dixit and Nalebuff argue that the trick to finding an equilibrium is figuring out what the focal point of the situation is:<\/strong> the aspect each party guesses the other will pick, and that each thinks the other guesses they\u2019ll<\/em> pick, in a circular loop of reasoning. Thus, it\u2019s often the most prominent characteristic. For example, if two people are told to meet in New York City but not told where or at what time, they\u2019ll likely choose to meet at noon, which seems like a \u201cstarting point\u201d time, and at a famous spot such as the Empire State Building. Studies show that when strangers are told to do this experiment, they\u2019re surprisingly successful at meeting up.<\/p>\n\n\n\n The game theory known as the tragedy of the commons<\/em> is a prisoners\u2019 dilemma that involves more than two people. It occurs when a group of people uses a common resource that\u2019s freely available\u2014for example, when hunters all hunt in the same area. Individually, each is incentivized to take as much as possible for themselves so they don\u2019t lose it to the other players who are also taking as much as possible. This ultimately causes the resource to be depleted and all players to lose.<\/p>\n\n\n\n Common resources can be conserved if all players limit their hauls, but, like with the prisoner\u2019s dilemma, only if all<\/em> of them do so. Incentives to cooperate are often destroyed by the <\/strong>free rider<\/em><\/strong> problem,<\/strong> where one person \u201ccheats\u201d and instead of limiting their haul, they continue to take as much as they can, knowing that because others are limiting their own hauls, they can take even more. Because all players are aware of this potential, all players are then incentivized to not<\/em> limit themselves\u2014because why should they limit their own take if doing so will unfairly enrich someone else? This again leads to a race where everyone takes what they can and the resource is depleted. <\/p>\n\n\n\n The tragedy of the commons underpins many of problems of society, leading to over-fishing, over-grazing, over-mining, and climate change\u2014each individual country has no incentive to stop their own polluting activities if the rest of the world continues theirs, as limiting themselves would set them back competitively economically.<\/p>\n\n\n\n So how can games be played to encourage players to make selfless choices so that common resources are not overexploited? Dixit and Nalebuff note that the common market, where everyone is free to act how they want, isn\u2019t good at encouraging things that benefit everyone but require sacrifices from everyone\u2014such as clean water and air. Thus, a system of governance or oversight must be established that watches for cheaters and punishes those who violate the rules. This is the only way users will feel confident others aren\u2019t cheating, and thus the only way they\u2019ll resist cheating as well. <\/p>\n\n\n\n Dixit and Nalebuff write that, to protect common resources while allowing their use: <\/p>\n\n\n\n After explaining the basics of sequential and simultaneous games and exploring the difficulty of getting players to coordinate, Dixit and Nalebuff discuss techniques that can help you get ahead in various types of games. These include introducing randomness to the game, detecting lies<\/a>, and limiting your options. <\/p>\n\n\n\n Dixit and Nalebuff write that, because other players are trying to predict your moves just as you\u2019re trying to predict theirs, you can make their job harder by acting randomly. If you can keep them from detecting patterns in your behavior, they\u2019ll be less likely to guess your next move. <\/p>\n\n\n\n The authors illustrate this principle with an example of a soccer player shooting a penalty kick. This is a zero-sum game with no Nash equilibrium\u2014there\u2019s no choice that benefits both kicker and goalie.<\/p>\n\n\n\n A ball takes only a fraction of a second to travel from the kicker\u2019s foot to the goal, so a goalie doesn\u2019t have the luxury of waiting to see which way it\u2019s headed before they have to choose which side to jump to. (Because a kicker rarely kicks to the center of the goal, Dixit and Nalebuff ignore this option and focus only on the option to kick either left or right.)<\/p>\n\n\n\n If a certain player always kicks to the left, the goalie will naturally guess that their next kick will also go left, and will jump left to block it. But if a kicker kicks left and right with no detectable pattern, the goalie will have a harder time choosing which direction to defend.<\/p>\n\n\n\n Dixit and Nalebuff note that it\u2019s difficult to be truly random. Most people unwittingly fall into patterns even when consciously trying not to. But there are a few tactics you can use to increase your randomness: <\/p>\n\n\n\n Dixit and Nalebuff caution that while randomness works well in games like sports, it\u2019s less effective in games like business. Negotiators don\u2019t usually value unpredictability.<\/p>\n\n\n\n Dixit and Nalebuff do note some real-life, non-sports situations where randomness is effective, though. Regulators, for example, can\u2019t check every single activity of every business or person they oversee, but random inspections can encourage people to comply with rules\u2014if a person thinks they might get caught at any random time, they\u2019re more likely to follow the rules at all times. This is the principle underpinning, for example, random tax audits, speed traps, and health inspections of restaurants.<\/p>\n\n\n\n Dixit and Nalebuff argue that you can\u2019t rely on others to tell the truth<\/a> if the truth could harm their interests. For example, if a salesperson recommends you buy the most expensive toaster available, you can\u2019t fully trust that their recommendation reflects their true opinion of the toaster if they work on commission and stand to make money on that sale.<\/p>\n\n\n\n In any strategic interaction, there\u2019s often an imbalance of information so that one player knows more than another player. Lies happen when the player who knows more tries to conceal or manipulate their information so as to gain an advantage over the other player. <\/p>\n\n\n\n Dixit and Nalebuff advise that if you\u2019re trying to determine if someone is telling the truth, you should look for their attempts at either signaling<\/em> (with actions that broadcast good intentions) or signal jamming<\/em> (with actions that limit the amount of information available to you). For example, if the salesperson above offers a no-questions-asked money-back guarantee<\/a>, that\u2019s a signal that the toaster is a good one. If they discourage you from looking at online reviews, they may be signal jamming to conceal negative information. <\/p>\n\n\n\nOverview of The Art of Strategy<\/em> by Avinash K. Dixit and Barry J. Nalebuff<\/h2>\n\n\n\n
Overview of Games<\/strong><\/h3>\n\n\n\n
\n
Zero-Sum and Non-Zero-Sum Games<\/strong><\/h4>\n\n\n\n
Sequential and Simultaneous Games<\/strong><\/h4>\n\n\n\n
\n
Sequential Games<\/strong><\/h3>\n\n\n\n
Simultaneous Games<\/strong><\/h3>\n\n\n\n
Cooperation Versus Competition in Simultaneous Games<\/strong><\/h4>\n\n\n\n
The Paradox of the Prisoner\u2019s Dilemma<\/h5>\n\n\n\n
\n
Dominant Strategy<\/h5>\n\n\n\n
The Nash Equilibrium<\/h5>\n\n\n\n
The Tragedy of the Commons<\/h5>\n\n\n\n
How to Encourage Cooperation and Coordination<\/strong><\/h4>\n\n\n\n
\n
Miscellaneous Game Strategies<\/strong><\/h3>\n\n\n\n
Be Random<\/strong><\/h4>\n\n\n\n
\n
Randomness in Everyday Life<\/h5>\n\n\n\n
Watch for Lies<\/strong><\/h4>\n\n\n\n