This paper attempts to give a systematic account of the ways in which games stage conflict and the strategic implications of these conflicts. This is done by presenting an incentive perspective which builds on the basic assumption that players prefer to win (in the broadest sense). This assumption is shown to be common in the game design literature and its explanatory strength, as well as its problems and limitations, will be discussed.

By discussing the implications of a view of players as maximizers the paper shows how the related concepts of conflict and strategy highlight often underappreciated facets of game design.   Games, or at least a large number of games, may be seen as structures of incentives inside which players attempt to optimize their outcome. In single-player games, the player plays against a hostile (or at least challenging) environment and in multiplayer games, players may be either pitted against each other, positioned as allies against the environment or placed in a relationship somewhere in between. In other words, games involve conflict or indeed an antagonistic relationship between competing powers. While viewing players as optimizers has obvious limitations (one being that such an approach generally has little to say about open-ended games without clear goals), this paper is based on the assumption that such a view is “good enough” to merit application as it opens analytical paths which enhances our understanding of conflict in games. Below, I will firstly argue that the assumption that player behavior is largely determined by the game goals is a basic one in the game design literature. Secondly, I discuss how games may be understood within the framework of economics and describe a series of implications hereof. In particular, the section stresses that the level to which a game can be said to be strategic depends on features not directly related to the game’s genre or to the number of players involved. Finally, I briefly discuss more general issues concerning the application of the analytical perspective outlined.

The assumption that players optimize
“It helps”, writes Greg Costikyan, “…to think of a game’s structure as akin to an economy, or an ecosystem; a complex, interacting system that does not dictate outcomes but guides behavior through the need to achieve a single goal: energy, in the case of ecosystems; money, in the case of economics; victory, in the case of a game.” (Costikyan 2002). Costykian here makes explicit an assumption or approximation common in game design literature: That players attempt (or may be thought of as attempting) to optimize their outcome (e.g. their score) in ways that are directly, if not deterministically, related to the incentives presented by the game. This level of explicitness is rare. More commonly, the assumption that players work to achieve the game’s goals seems so basic that it is not even mentioned. But nevertheless, it is often evident. Richard Rouse defines gameplay as “the degree and nature of the interactivity that the game includes, i.e. how players are able to interact with the game-world and how that game-world reacts to the choices players make” (Rouse 2005). According to Rouse it follows that “In Doom, the gameplay is running around a 3D world at high speed and shooting its extremely hostile inhabitants, gathering some keys along the way. In San Francisco Rush, the gameplay is steering a car down implausible tracks while jockeying for position with other racers”. But of course this only follows under the assumption that players work towards the game goals. Without any behavioural assumption, the Doom player might as well be expected to turn around in circles or continuously fire his weapon into the nearest wall.

Rolling and Morris, in their Game Architecture and Design, consider it useful to assume that any strategy available to a player must have both advantages and disadvantages: “If there’s only a downside, no one will ever use that strategy so why bother including it in the game?” (Rollings and Morris 2004). While admitting that the player may have priorities not exclusively linked to succeeding in the game, Rollings and Morris maintain that features which do not further the player’s success will soon be abandoned by players. In a related argument, Juul is explicit that “A bad game is one where the player is unable to refine his or her repertoire or where a dominant strategy means that there is no reason to improve the repertoire.” (Juul 2003). This implies that players want to succeed in the game, since a game with a dominant strategy need not be bad if only players preferred tapping buttons to match the game soundtrack instead of optimizing their outcome [It also implies that players wish to succeed only given certain conditions related to game balance. I will not go into this discussion here.]

The above examples indicate that the notion of players as outcome maximizers is prevalent among certain influential authors who have reflected on game design. For some, it even seems too basic to deserve mention explicitly. Much may be said against this view of player behaviour, and we shall return to this discussion later, but to grasp the full implications of this view it is worth turning to economics, a field which has this assumption at its core.

Game design and economics
As Costikyan argued, there is a close affinity between economics and game design. So close, in fact, that game design could arguably be considered an application of economic theory and each game session a case of experimental economics [Most obvious in the case of games with clear goals, and far less obvious in “process-oriented” games (Egenfeldt-Nielsen, Smith, and Tosca In press) such as MMORPGs which do not specify clear goals]. Classical economics considers the case of an individual attempting to maximize her outcome in accordance with her preferences. For instance, this person (we shall call her Alice here for convenience) might consider whether to save for her pension, taking into account her desire to actually retire, the annoyance of needing to put aside money each month, her perceived risk of not living until pension age etc. Though such considerations may be complex, the optimization principles at work are fairly simple as Alice’s choices do not affect the variables of the equation. In a sense, the task facing Alice is structurally comparable to that of the player of the arcade game Moon Patrol.

Figure 1 – Moon Patrol (Irem, 1982)
The player combats an environment which does not adapt strategically in response to the player’s actions  

In Moon Patrol, the player controls a purple space buggy avoiding rocks and holes while battling aliens attacking from above. The player, then, optimizes her outcome against an environment which does not adapt to counter her strategy (although it does change according to her actions). She can concentrate on doing her best without worrying that someone or something guesses her intentions or adapts to her playing patterns. Returning to Alice, instead of pension funds she is now considering whether to donate to a charity. The charity in question will only be successful if a certain number of people donate (otherwise the income will be lost to administration). Thus, Alice might stand to lose the money, but her donation might help inspire others to donate as their investment becomes safer. The outcome maximizing course of action here is dependent on the actions and perceptions of others. This optimization problem is the domain, not of classical economics, but of the disciplinary branch known as game theory (for a discussion of the relationship between economic game theory and video games see Smith Forthcoming). Game theory is the study of the outcome of conflicts between agents who are interdependent; conflicts in which the outcome for participants depend on the aggregate of their choices. We can say that game theory is about strategy, understood as choices made based on assumptions of other participant’s assumptions and actions.

Alice’s charity dilemma is comparable to video games in which someone (other players) or something (the environment) adapts to counter the player’s strategy whether that strategy be real or perceived. In Counter-Strike, for instance, the outcome of one’s choices depend clearly on the actions of the opposing players whose actions are again likely to depend on their assumptions about the actions of their enemies. We shall return to this issue below, but so far we can see that the player of single-player games with non-adaptive environments is engaged in an activity analysed by classical economics, while the player of multi-player games in which the opposition can adapt are in a sense doing game theory.

Does it then follow that the player implied in many reflections on game design corresponds to the Homo economicus of traditional economics? To a high extent, that does seem to be the case. But we need to consider what the entities are in fact trying to achieve; what they see as the goal. In classical economics, the agent is seen as an emotionless optimizer who attempts only to increase his or her own utility, typically in the form of profits. This agent does not (or should not) care about how others fare; being neither envious nor gloating since this is inconsequential to its own profit. There are numerous problems with this view of economic behaviour (see for instance Frank 1988) but we can see that it would correspond to the idea that gamers merely attempt to maximize their score, even in multiplayer games. Thus, such a gamer would not care about whether she was beaten to the finish line in Gran Turismo 4 but would only be focused on her achievement, her completion time (relative to her personal record, for instance). While probably rare in its pure form, such behaviour is not unthinkable. In fact, actual players of multiplayer games may often place value both on defeating other player(s) and performing well in more absolute terms. But for present purposes we may wish to consider them distinct modes of victory and they will be referred to as “social victory” and “personal victory” in the following.

The incentive perspective
Understanding players as goal-oriented entities and looking at games through the lens of economics means applying an “incentive perspective”. In the following, I will describe what such a perspective may entail [The proposed characteristics may be said to represent a “strong” incentive perspective. One may of course analyze incentives based on different assumptions and topics of interest]. Firstly, the incentive perspective is one way of looking at video games. It is an analytical stance and as such does not claim to be more complete or correct than other perspectives. It is blatantly rule-biased and so tends to ignore audiovisual and narrative concerns. But it is also a formalization of core assumptions in game design and one without which general discussions of the relationship between game design and player behaviour become difficult.

Let us be precise about the assumptions involved. An incentive perspective (IP) assumes that players attempt to succeed in accordance with the goals presented by the game (that their preferences are determined by the game). IP considers as “player” any entity with unique preferences and this entity can consist of several individuals (e.g. a team). For analytical purposes the game environment in single-player games may be thought of as a player which tries to hinder the success of the (human) player. Also for analytical purposes, IP is only concerned with in-game interaction and does not take into account the actual level of play on which players may interact in various ways beyond what the game itself specifies. Each player applies a cause of action (or strategy) and this cause of action, in relationship with the features of the game environment and the actions of other players determines the outcome for each player. The guiding question of IP is this: Given the options available to other players, how will a given player (as described above) act given the incentives structure of a game?

No particular method or approach is inherently appropriate to answer this question. But I will suggest here that an analytical approach inspired by game theory. This approach has three focus points: Conflict type, number of players and player interdependence.

Conflict type
In Pong, the conflict between the two players resembles that of classical two-player games like Chess, Backgammon, and Kalaha. One player’s gain is the other player’s loss. In the language of game theory, this conflict is “zero-sum” as the combined gains and losses equal zero [In Pong, of course, the sum of the final scores does not equal zero. But in the end, one player wins while the other player loses.] In two-player zero-sum games, outcome maximizing players will not cooperate. The game simply gives them no incentives to do so and thus, issues of trust and deceit do not come up. If Alice is maximizing her Pong score and believes that her opponent Bob is being equally self-concerned, she should not believe any promise from Bob that he will hit the ball gently on the next rebound. She needn’t consider whether Bob is trustworthy; she can simply assume that he is not.

In many games, players are placed in less antagonistic relationships. In Joust, the two players are placed in a situation where the likely outcome of a strategy depends highly on the strategy chosen by the other player. While there are good reasons to cooperate, there is also a temptation to attack the other player (Smith 2005). This is characteristic of non-zero-sum games, games in which the combined score is dependent on the aggregate of player choices. Pong would have been non-zero-sum if the game had also somehow rewarded players for merely keeping the ball in play (and would have had an issue of trust if the reward for eliminating the other player exceeded the probable gain from keeping the ball in play). A two-player non-zero-sum game need not have trust issues at all. Another version of Joust, let us call it Harmonic Joust, might not award any points for killing the other player and have the challenge set up in a way in which the loss of the other player was more of an inconvenience than it was an advantage (the advantage being having more potential rewards for oneself). In Harmonic Joust, the players would have no incentives to attack one another. Rather, the players would mostly be “incidentally” sharing the same space while fighting their own personal battles against the game environment. Of course, this is only true to the extent to which the player applies personal victory criteria.

Number of players
The way a conflict plays out depends not only on its sum type but also on the number of players involved. In game theory, a game like Moon Patrol would be described as a “game against nature”. Here, nature is considered a player albeit one which acts without intentionality and assumptions. This is different from single-player games in which the environment actually responds strategically (in the sense used above) such as Far Cry. In Far Cry, the enemies have a number of strategies and, from the perspective of the player at least, may be said to adapt to a perceived threat. While two-player games are often relative easy to model and explain, interesting things happen when more players join. The crucial change from two-player games is that it may now, even in zero-sum games, well be in the interest of players to form coalitions.

Figure 2– FourPong (SquareFuse, 2003)

FourPong (see Figure 2) is a four-player two-ball Pong clone in which a player takes on three computer-controlled opponents. The rebound angle of the ball is determined by the portion of the bat on which it strikes, making players able to control the angle to a certain degree. The computer opponents may not be sophisticated strategists and they certainly are not sophisticated communicators but if the game were played by four human players there would be various ways for groups of two or three players to ally temporarily against one or two other players. To take another example, players (or teams) in the real-time-strategy game Age of Empires II may be playing a game type in which there will be only one winner. Thus, no team shares any ultimate goal with any other team but in a three-team game two teams may well be tempted to join forces to eliminate a common threat. This is particularly likely to happen the moment one player significantly pulls ahead of the opposition as losing players realize that fighting internally would mean certain defeat.

Player interdependence
Games differ as to the level of interdependence between players. In fact, the level of interdependence may be said to correspond to the level to which a multiplayer game is strategic. Certain early arcade games represent the lowest possible degree of strategicness. For instance, the game Time Pilot offers the option of a two-player game, but in this mode players merely take turns to play and each player’s score is displayed in the upper corners of the screen (see Figure 3). Thus, one player’s actions do not directly influence the possibilities of the other player; indeed the two game spaces are kept entirely separate. The only in-game interaction is through the displayed scores which may prompt players with relative winning preferences to alter their play (say, towards more chancy moves if one falls far behind).

But also multiplayer games in which the players share the same gamespace may differ significantly in the degree to which players are strategically interdependent. In the game Super Monkey Ball, players control monkeys encaged in rolling balls and must compete in various disciplines. One of these is a race in which players must try to cross the finish line quickly and may collect various bonuses and weapons/upgrades on the way. Now, playing against flesh-and-blood opponents may certainly matter to a player in terms of the gaming experience but in the Super Monkey Ball race (as in most racing games) the space for strategic choice is slim. One may choose to focus on collecting weapons and on attempting to push opponents over the edge of the course, but for the better part of the race, one will be attempting to cross the finish line as quickly as possible. The existence, and the choices, of other players within the gamespace, while not insignificant, have only a limited influence on a player’s likely strategy. In this sense, Super Monkey Ball corresponds closely (but not entirely due to the weapons etc.) to a 100m sprint in the physical world. Sprinters do influence each other to a certain degree but essentially they are attempting to cross the finish line as quickly as possible.

In Age of Empires II, the player relationship is crucially different. Here, the final outcome of almost any player choice depends on the choices of the other players. Although certain strategies are virtually always bound to be unsuccessful (such as remaining entirely passive, building no structures) the game has a large possibility space in which the consequence of a given choice is dependent on the other players. To give but one example, an early attack using mounted units is often a powerful strategy, but not if the victim has trained the inexpensive pikemen units which counter cavalry well. Thus, whereas Super Monkey Ball corresponded to 100m sprint, Age of Empires II correspond to a soccer match in which the outcome of strategic choices can only ever be predicted based on assumptions of the strategies employed by the opposing team.

It should be noted here, that the level to which a game is strategic does not depend on whether it belongs among what is usually called strategy games. Tekken and Jetmen Revival (see Figure 4), for instance, are both action games with strong strategic elements.

Figure 4– Jetmen Revival (Crew42, 2003)
Each player controls a small aircraft initially positioned at one side of the gamespace. While seemingly a simple game, different ways of scoring and the limited nature of player resources (health, fuel, and ammunition) pave the way for a significant number of strategies.

However, a game can only be strategic if it is a “game of emergence” in Jesper Juul’s terms (Juul 2002), that is a game in which variables interact dynamically (as opposed to the game being a predetermined series of event with predetermined outcomes). Figure 5 shows four game types placed on a continuum from “Unstrategic” to “Highly strategic”.

Figure 5 – Game types placed on continuum based on level of player interdependence or “strategicness”. Singleplayer games with adaptive environments are strategic to the extent that the environment adapts or learns. Their place on the continuum is a question of the specifics of the AI.  

As mentioned above, the strategy chosen by a player is a function of the game incentives, the actions of other players and assumptions about the future choices of other players. In all four game types shown in the figure above, certain courses of action may be inherently better than others. We could imagine for instance that Moon Patrol was designed in a way that it would always pay to drive and shoot as fast as possible (not the case in the actual game). This would make the playing experience a monotonous one. But there is a special feature of multiplayer games with high player interdependence: Certain designs may render unimportant assumptions about the choices of the player. For instance, Age of Empires II might be designed in a way in which player A was certain that player B would play in a certain way. This would be the case if the game had a “dominant strategy”, a course of action which, no matter what other players do, is always the most powerful (see also Rollings and Morris 2004; Smith Forthcoming). Such a strategy will lead to a “strategic equilibrium”, a state where players will not change their strategies and (again depending on the details of the design) the game will become highly predictable. Strategic equilibrium can be illustrated by turning to a “spatial” model of the classical game theory situation The Prisoner’s Dilemmas [The reader unfamiliar with the Prisoner’s Dilemma may wish to consult Robert Axelrod’s lucid description (Axelrod 1984).]:

Figure 6 – The Spatial Prisoner’s Dilemma by Serge Helfrisch (www.xs4all.nl/~helfrich/prisoner)
A strategy is marked by a colour, the essential ones being blue and red.

In this simulation, thousands of cells are playing the Prisoner’s Dilemma against each other continuously. Each round a cell chooses the most successful of two strategies played in its immediate surroundings on the previous round. The screenshots are from the advanced stages of games, which differ only nominally in the size of the advantage awarded to a cell playing blue against a cell playing red. Only within a very small interval does the game lead to behaviour which is “unpredictable” and where neither strategy dominates entirely. �Both on the left and on the right we have a stable strategic equilibrium in which no player will change his or her strategy. Although there are other variables at play, equilibrium situations result from by far the majority of possible game “rule sets” illustrating how difficult game balance can be to achieve.

Discussion
The incentive perspective is a formalization of certain assumptions found in game design discussions, the most important one being that players wish to win. Similar assumptions lie at the core of other fields but have been contested and often proven problematic when used to analyze actual human behaviour. Not rarely, for instance, have “rational choice” approaches in the social sciences been criticised for being deeply flawed (e.g. Green and Shapiro 1994) while the classical rational agent model of economics has been shown not to �correspond too well with experimental results (Kagel and Roth 1995; Frank 1988). There is little doubt that understanding people as utility maximizing entities only is a problematic and imprecise approach. In terms of games, it is also clear that players simply do not always devote all their resources to achieving the goals set up by the game designers. Real players can be explorative, artistic, have reasons for losing a certain game on purpose, set up their own goals etc. Thus, the question is not whether the incentive perspective is capable of explaining or predicting all facets of player behaviour, whether it is the capital-T Truth. It cannot and it is not. Of course, for game designers it may work well as a guiding approximation without being completely “true” and without such an approximation constituting disregard for the various “rebellious” uses that games are also put to. Indeed, an incentive perspective on player behaviour may serve as a minimal theory of players, without which constructing enjoyable games becomes difficult but which is not capable of serving as a complete guide to game design and which can easily be misleading if thought to be a description of actual play.

For game scholars interested in the social dynamics of gaming and of the actual patterns in player behaviour, the general goal-orientedness of players remains a hypothesis which is curiously understudied. At present, we have a very limited systematic understanding of the uses to which games are put by their players, and indeed of the ways in which players interact while playing. In light of the often surprising results that similar questions have provided in related fields (such as television studies) these issues seem worthy of more rigorous attention.

Conclusions
Initially, this paper argued that the assumption that players prefer to win is common in game design literature. It was noted that similar assumptions lie at the heart of economics and ideas from this field were briefly applied to video games to sketch the analytical implications of viewing games as incentive structures.�In particular, it was noted that the relationship between players in multiplayer games span a large spectrum and that games vary greatly in the extent to which players are strategically interdependent; that games may be placed on a continuum of strategicness.

Finally, the status of the “incentive perspective” was discussed and it was noted that what may serve as a useful approximation to game designers still remains largely unexplored territory in terms of the scholarly study of player behaviour and interaction. At this point, the understanding of the activity of the player (both theoretically and empirically) seems underdeveloped within game studies. In particular, the relationship between game design or structure and player behaviour is virtually unexplored but likely to offer fertile ground for future studies interested in bridging gaps between formalist analysis and behavioural approaches.  

References  
Axelrod, Robert. 1984. The Evolution of Co-operation. London: Penguin Books.

Costikyan, Greg. 2002. I Have No Words & I Must Design: Toward a Critical Vocabulary for Games. Paper read at Computer Games and Digital Cultures Conference Proceedings, at Tampere.

Egenfeldt-Nielsen, Simon, Jonas Heide Smith, and Susana Pajares Tosca. In press. Understanding Video Games. New York: Routledge.

Frank, Robert H. 1988. Passions Within Reason - The Strategic Role of the Emotions. New York: W. W. Norton & Company.

Green, Donald P., and Ian Shapiro. 1994. Pathologies of rational choice theory : a critique of applications in political science. New Haven: Yale University Press.

Juul, Jesper. 2002. The Open and the Closed: Games of Emergence and Games of Progression. Paper read at Computer Games and Digital Cultures Conference, at Tampere.

———. 2003. Half-Real - Video games between real rules and fictional worlds. PhD dissertation, Department of Digital Aesthetics and Communication, IT University of Copenhagen, Copenhagen.

Kagel, John H., and Alvin E. Roth, eds. 1995. The Handbook of Experimental Economics. Princeton: Princeton University Press.

Rollings, Andrew, and Dave Morris. 2004. Game Architecture and Design - A New Edition. Boston: New Riders.

Rouse, Richard. 2005. Game Design - Theory and Practice (second edition). Plano: Wordware Publishing.

Smith, Jonas Heide. 2005. The problem of other players - in-game collaboration as collective action. Paper read at DIGRA 2005 - Changing Views: Worlds in Play, at Vancouver.

———. Forthcoming. The games economists play - implications of economic game theory for the study of computer games. Game Studies.  

Video games cited
Age of Empires II: The Age of Kings (Ensemble Studios, Microsoft Game Studios, 1999)

Counter-Strike (Valve Corporation, Sierra On-Line Inc., 2000)

Doom (id Software Inc., 1993)

Far Cry (Crytek, Ubisoft Entertainment, 2004)

FourPong (SquareFuse, 2003)

San Fransisco Rush (Atari Games, 1996)

Gran Turismo 4 (Polyphony Digital, SCEI, 2004)

Jetmen Revival (Crew42, 2003)

Joust (Williams, 1982)

Moon Patrol (Irem, 1982)

Pong (Atari, 1972)

Super Monkey Ball (Amusement Vision, SEGA Entertaintment Inc., 2001)

Tekken (Namco Limited, 1995) Time Pilot (Centuri, 1982)