The Evolution of Cooperation*. *. Robert Axelrod. Professor of Political Science and Public Policy, University of Michigan, Ann. Arbor. Dr. Axelrod is a member of . The Evolution of Cooperation THE EVOLUTION OF COOPERATION Robert AxelrodBasic Books, Inc., PublishersNew York A. Accessed: 11/02/ Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.
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Five mechanisms for the evolution of cooperation: Kin selection. Direct reciprocity . Indirect reciprocity. Spatial selection. Group selection. The evolution of cooperation can refer to: the study of how cooperation can emerge and persist .. Axelrod, Robert; Hamilton, William D. (27 March ), " The Evolution of Cooperation" (PDF), Science, – File:Axelrod Robert The Evolution of peypredkoefritlec.ml peypredkoefritlec.ml (file size: MB, MIME type.
An earlier version of chapter 5 appeared in Science 27 March l Copyright by the American Association for the Advancement of Science. The evolution of cooperation. Iiibliography: p. Games of strategy Mathematics 3. Conflict management. Consensus Social sciences 6. Social interaction.
A89 '. Currently, he is a professor at the University of Michigan and the author of several books. In , Axelrod obtained his B. Robert loves to use real-live vivid examples to give a brief explanation on how collaboration or alliance can change. Is it worth it to show compassion and kindness to those who are not willing to repay the favor?
The discrimination of others may be among the most important of abilities because it allows one to handle interactions with many individuals without having to treat them all the same, thus making possible the rewarding of cooperation from one individual and the punishing of defection from another What advice would you give to other people?
Or if you want to take the matter even further — How should a country react to Blitzkrieg — a hostile invasion without a declaration of war?
Evidently, each of these boxes represents a different case or scenario that illustrate an outcome inherent in such circumstances: Box 1: Cooperation column and cooperation row — The first box manifests the highest form of cooperation, and in doing so — each player receives 3 points for its efforts.
Box 2: Defection column and cooperation row — The second box entirely differs from the first one on the ground that unity is not always the best solution. The column player decides to abandon the coalition, but the row participants strive for cooperation. In doing so, the row player receives zero points, and then defecting column player win the game by accumulating 5 points. Box 3: Cooperation column and defection row — This box, in fact, represent the inversive process of the previous box.
The column player now is pressing for cooperation and the row player chooses to seize the momentum and defect. The winner is the row player with 5 points, and the column player goes home empty-handed. Box 4: Defection column and defection row — In the last case scenario where both players decide to defect and go on their own, each of the participants receives P — which stands for punishment.
They both get one point.
As the process exhibits, the game awards the players based on their decision during the show. However, the worst part is that the participants can manipulate the system. For instance, if the game is played once, both players will logically choose defection to accrue the benefits of one-time play.
If the play is extended to several rounds, the decision-making becomes more rational. If the participants settle upon the idea of embracing cooperation, they will maximize their points!
One can see the beginning of the formation and the subsequent dissolution of a cooperator cluster. Cooperators are in blue and defectors are painted red. A light blue cell stands for a cooperator that was a defector in the previous round and an orange cell indicates a defector that was a cooperator in the previous round.
Full size image Having discussed the emergence and decay of cooperation in general, taking into account previous experiments and intuitions, we now turn to the unique feature of our setting: the possibility of changing position that is offered to the participants and its possible effects on cooperation. In all our sessions, we have observed that players are rather mobile during the first rounds but, as time goes by, mobility decreases slightly and they tend to settle at some position in space, although movement never ceases until the end of the experiment see SI, Fig.
In fact, a cooperator, unless she finds herself in a very favorable cooperation environment, tends to escape from incoming defectors.
Conversely, a defector moves because she is always seeking a cooperation environment in order to accumulate more payoff. We also note that mobility and the incentive to aggregate in order to get a larger payoff cause the mean degree to increase from the initial value of 2. Figure 5 shows the corresponding plots for the number of cooperators a and defectors b in the neighborhood, and the total number of neighbors c , in the previous round.
Figure 5 Average mobility of players in their neighborhood. Frequency of movement of cooperators and defectors as a function of a the number of cooperators in the previous round, b the number of defectors in the previous round, and c the total number of neighbors in the previous round. Full size image In Fig. This is in agreement with our interpretation that both cooperators and defectors aim at a cooperator environment as far as possible.
Cooperators move more than defectors because the latter are more satisfied with a given number of cooperator neighbors.
Figure 5b is somewhat more difficult to understand, but it shows that the mobility behavior of both cooperators and defectors is similar when the number of defecting neighbors is between two and four; however, the mobility of cooperators increases when this number is more than four. In this case cooperators feel exploited and try to evade defectors although the average number of free cells around them tends to decrease.
In the other limit, i.
This happens because with a mean degree tending to about four, there are about one or two other cooperators around the focal one and so the latter is relatively satisfied and shows less tendency to move. A caveat is in order here regarding mobility when many neighboring sites are occupied, irrespective of the actions of the individuals occupying them: in those situations, players have less options to move in fact, if they were completely surrounded, which is an uncommon phenomenon, they would not be able to move at all.
This is reported in Fig. This is an extra factor influencing their behavior and can also be responsible for part of the decay of the mobility in panels a and b. A complementary view of local mobility is provided by Fig. It appears that mobility is maximal when this difference is around 0, meaning one cooperator less than the number of defecting neighbors. This is understandable because in this situation neither a cooperator nor a defector is satisfied. For instance, if a cooperator has a defector and another cooperator in his neighborhood, he will tend to move closer to the cooperator.
However, as discussed above, when there are many cooperators and defectors in the neighborhood, most of the time the movement cannot take place, either because of collisions or because of lack of free cells. At the extremes of the curve, where the difference is large in absolute value, either the neighborhood is too crowded to allow movements, or the cases are rare and have a high standard deviation, or they have not been observed. An interesting observation arising from Fig.
Figure 6 Frequency of movement of cooperators and defectors as a function of the difference between the number of cooperators and defectors in their neighborhood in the previous round.
Full size image Discussion In this paper, we have presented the results of an experiment intended to shed light on the hitherto unclarified issue of the relevance of mobility in a geographical context to cooperation. In particular, important differences between random and purposeful motion in their ability to support cooperative interactions had been reported from a theoretical viewpoint, but experimental counterparts to those results were lacking.
In fact, a vast majority of players can be classified as defectors or as moody conditionally cooperators, i. This indicates that the possibility to move around in space does not change very much the way players choose their actions.
Remarkably, our experiments also contribute to the understanding of the possible assortment of cooperators in order to support cooperation. The numerical simulations reported in 31 suggested that cooperators may survive by forming clusters in which they mainly interact with other cooperators. In our experiments, we have indeed observed that such clusters appear with non-negligible frequency; however, their lifetime is quite limited because the possibility to move allows cooperating agents at the boundary of the cluster to separate from it to severe their interactions with defectors, or to choose defection themselves.
A dynamical network view can also be taken considering that, at each discrete time step, one could draw a spatial graph in which edges connect players that are neighbors. At the next time step, owing to mobility, some links might disappear while new ones may be formed.
This, except for the topological constraints imposed by two-dimensional space, compares with dynamical relational networks. In contrast to what has been observed on the latter 21 , 22 , where allowing players to cut and make links at will does lead to clustering of cooperators and to an increase of the cooperation level, in our experiments we measure a much lower amount of cooperation.
The reason can be traced back to the fact that, if links evolve indirectly by motion of the players in geographical space, they cannot be cut one by one, and when moving away from defectors cooperators also cut their links to cooperators. Therefore, we conclude that for clusters to be an important factor in the promotion of cooperation, individuals must have complete control on their choices of partners, a condition that has never been put forward before.
We believe that this behavior is connected to the observation in the previous paragraph: players realize that the decision to move has very frequently pros and cons as it affects their connections in an indiscriminate manner, and at some point they conclude that they are not going to find a safe haven against defectors. On the other hand, it is worth noticing that in our experiment there is no punishment for interacting with a defector, and therefore all the residual motions observed in late stages must arise from spite, i.
This is in agreement with our finding that cooperators tend to move somewhat more often that defectors, implying that while the latter just move trying to find others to exploit, cooperators have the additional motivation to punish defecting partners.
In addition, we have also observed that mobility of all players is maximum when there is more or less the same number of cooperators or defectors in the neighborhood. Of course, to interpret these results one needs to bear in mind that when a player has many partners her mobility is also reduced by the lack of available cells to move to.
With this caveat, it appears that when the number of cooperators and defectors is approximately the same around a given subject, she will try to move to increase her interaction with cooperators irrespective of her own action, as can be expected.
On the contrary, when there are many neighbors of the same type, mobility becomes less relevant and perhaps impossible, this being the reason why we observe a maximum. In conclusion, we stress that the interaction between behaviour and mobility does not seem to increase the level of cooperation in a human population set on a geographical framework.
The main reason for this phenomenon turns out to be the fact that setting and breaking links cannot be done independently for every player as the mechanism for rewiring is motion in space. Interestingly, these results pose important questions about the emergence of cooperation in neighboring human groups, which could be most relevant in interactions in a socio-ecological context among hunter-gatherer groups, either in our recent evolutionary past or in presently existing populations.