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Designing for Motivation
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Designing for Motivation

June 7, 2007 Article Start Previous Page 4 of 5 Next


Motivation on the Player State

In general, shoot'em up and beat'em up style games are based on the player state P. The player’s characteristics (character, ship, etc.) are upgradeable but are not permanent. Everything the player can acquire is temporary. He can lose his bonuses at the end of the level or when he dies. The player’s objective is to keep his strength as high and as long as possible to defeat the final boss.

The need N of the player is high, and he has to increase his strength P through bonuses and upgrades. Challenge expectation C is directly linked to P: weak when the player has no upgrade and strong when he acquires enough bonuses. The reward R is represented by a consequent increase of fire power and hence by the decrease of the difficulty.

Since the difficulty is here independent from the player state P, the motivation of the player decreases with his strength to finally disappear when the difficulty rises too high. We quickly reach a selection between the strongest and the most perseverant players that progress and the others that give up.

There exists some efforts in these types of games to make them more accessible, including permanent bonuses, adaptation of the difficulty level, and the possibility to win without upgrades. For now, however, core gamers remain the main target of these kinds of games.

Mixed Motivation

Most games build their motivation on the four PNCR functions. Because these functions are interdependent, to succeed in an efficient management of motivation one has to balance the four parameters. It is a question of tweaking and tuning to achieve the desired game experience.

id Software's first person shooter Quake IV features mixed motivation

For example, online first person shooters like Quake or Unreal Tournament have a mixed motivation that is based at the same time on the challenge C (tournaments Game play), the need N (arms/ armor/ ammunition/ health), the player state P (bonuses and temporary boosts), and the reward R (score and the victory).

In the context of tournament and competition experience, even if the main motivation is the victory, the balance among the four functions is essential.

IV. PNRC Applications


To achieve motivating game systems, we need to take into consideration every PNRC variable and to ensure the follow-up of the motivation for each challenge. We can conceive the game structure as a succession of iterations of challenges along the difficulty curve. Each iteration is linked from the previous to the next one by the player state P.

The player state allows retrieving necessary information for the adjustment of the "challenge" and of the "reward," the reward responding to the "needs" created by the game design. The objective is to increase or at least to maintain the motivation of the player during his progression.

The motivation will change from iteration to iteration, and it will increase or decrease, according to adequacy of the game compared to player’s expectations. To keep the player motivated, the reality of the game system (S) must be equivalent to the expectation (E) of the player.

If at the beginning of each iteration of challenge, the player starts with motivation Mprev, let’s then call Mnext the motivation at the end of the iteration:

There is: S <> E (Equivalency)

N and P are constants at the considered time:

S = (RS*CS) E = (RE*CE) (Definition)

And: CS <> RS > N (Conditions)

S is the motivating factor from the system parameters and E is the player’s expectations. P (player state) represents the player’s strength at the given time, N his needs:

  • The motivating value of the system has to be equivalent to player’s expectation.The challenge has to be proportional to the reward and, if possible, slightly superior to player’s capacities.The reward has to fulfill player’s needs and has to give him a bonus that is relative to the difficulty of the challenge.

  • The challenge has to be proportional to the reward and, if possible, slightly superior to player’s capacities.

  • The reward has to fulfill player’s needs and has to give him a bonus that is relative to the difficulty of the challenge.
The resulting player's motivation, Mnext, is then the initial motivation, Mprev, affected by the ratio of system and expectation:
Mnext = (S/E)*Mprev
Note: if the ratio of S/E is constant during the whole game then the motivation is a geometric progression: un+1 = q un

For example, if a player can have no expectation on needs, N = small, but he can have a big expectations of challenge, CE = large. If CS <> CE, even if his needs are fulfilled, the system still has to take into account his performance (RS > RE > N), otherwise S is smaller than E and the motivation Mnext decreases.

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