It’s all about who you know
Why do people cooperate with each other? Why do animals work together? How can cooperation survive when there are opportunities to cheat? An important observation in these questions is that it matters who you interact with. If you are likely to interact multiple times with the same people, it may be better to cooperate rather than to cheat (see also Axelrod and Hamilton, 1981). The script on this page (open script in separate tab) shows an implementation of a small number of repeated simple spatial games, both cooperative and competitive, and allows you to experiment with some of the spatial features. The controls for this script are explained at the bottom of this page.
Repeated simple spatial games
The script on this page simulates agents that repeatedly play simple spatial games. These spatial games are called simple because they are:
- two-player, since every interaction is between two individuals;
- symmetric, since the outcome of each interaction depends only on the actions of the players, and not on who these players are; and
- simultaneous, since both players select their action at the same time so that no player can react to the action selected by the other.
Individuals are organized on a grid, which may or may not include empty spots. Each individual has a colour that identifies what strategy it is playing. In the Prisoner’s Dilemma, for example, red individuals defect while blue individuals cooperate. At every time step, every individual plays the simple game with each neighbour within its interaction neighbourhood. Both the range and the shape of the interaction neighbourhood can be set within the script. The outcome of each game depends only on the strategies used by the two players, which can be seen in the payoff matrix in the bottom right of the script. In the Prisoner’s Dilemma, for example, when a cooperator plays against a neighbouring defector, the cooperator gets a score of -1, while the defector receives a score of 4.
All individuals play the game with each neighbour in their interaction neigbourhood. Once all games have been played, individuals look around to determine the neighbour in their vision neighbourhood that has the highest average score. If that score is higher than the individual’s own score, the individual switches its strategy to match the one of their neighbour. If there happens to be more than one neighbour with the highest average score, the individual will randomly pick one of those neighbours’ strategies. However, if one of the strategies that yields the highest observed score is the agent’s own strategy, the agent will always decide to keep its own strategy. This means that a cooperator will choose to stay a cooperator as long as the highest average score that he can see is obtained by another cooperator, even if the individual himself gets a very low score.
The script allows you to simulate a number of different games. The Prisoner’s dilemma, Hawks and doves, and Hawks, doves, and retaliators games are cooperative dilemmas. In these games, all individuals would benefit from the cooperation of other agents, but experience an individual incentive to defect on cooperation. In these cooperative dilemma’s, you can see how “clusters” of cooperation can grow to overcome defectors. What allows these clusters to survive is that in the center of such a cluster, cooperators only interact with other cooperators. That is, individuals inside a cluster cannot be cheated by faraway defectors.
In contrast, Rock-paper-scissors, Rock-paper-scissors-Spock-lizard, and Elemental RPS are purely competitive settings. In each of these setting, each action is the best response to another action so that there is no single optimal action to play. As long as all strategies continue to exist in the population, agents keep changing their strategy to keep up with other agents changing their strategy. In the simulation, this will show itself as waves moving through the population. This kind of behaviour is also the basis of the reason why theory of mind can be helpful in settings such as Rock-paper-scissors (see also theory of mind in Rock-paper-scissors).
Finally, the Coordination and the Stag hunt are purely cooperative settings. In both of these settings, agents want to cooperate, but either do not know how to cooperate (Coordination game), or are afraid that the other may not cooperate (Stag hunt game). In these games, the simulation tends to quickly settle into an equilibrium, where no agent wants to change its strategy. Whether or not there are multiple actions that survive in this stable solution depends on the number of empty spots, the vision neighbourhood, and the interaction neighbourhood.
- Individuals on the grid: You can manually switch the strategy of an agent by clicking on it.
- Strategy frequency graph: After every change in the situation in the grid, the graph updates to show the new frequencies of each strategy among agents.
- Game selector: Selects the game that will be played by the agents on the grid. Changing the game will also reset the field.
- Agent count slider: This determines the number of agents on the grid, between 1500 and 2500 individuals. Since the grid is 50 by 50 agents in size, 2500 agents fills up the grid entirely. When you change the number of agents, the strategies of remaining agents are not changed.
- Interaction slider: This slider determines the size of the interaction neighbourhood of every agent .
- Interaction neighbourhood shape toggle: By clicking on the interaction neighbourhood shape icon, you can switch between a cross-shaped (Von Neumann) and a square-shaped (Moore) interaction neighbourhood.
- Vision slider: This slider determines the vision range of every agent.
- Vision neighbourhood shape toggle: By clicking on the vision neighbourhood shape icon, you can switch between a cross-shaped (Von Neumann) and a square-shaped (Moore) vision neighbourhood.
- Simulation speed slider: This shows how quickly changes occur in the grid, varying from one change per 2 seconds to up to 50 changes per second.
- Play one round: Plays a single round of simulation.
- Start/stop simulation: The simulation will automatically stop when after a round, no agents changed their strategy. Starting the simulation does not reset the board. This means you can pause the simulation to make some manual changes if you want, and continue from the situation you have created.
- Reset field: Randomly re-assigns empty spots across the board, and strategies across agents.
- Change payoff matrix: By clicking on the star (*) near the payoff matrix, you can change the payoff matrix as you see fit. Note: the new matrix is not checked for validity. Make sure you enter a valid payoff matrix.