During the first four billion years or life on Earth, the primary means of information transfer was genetic. (M. Nowak)

• Game Theory and Animal Behaviour
• Autonomous Agents and Multi-Agent Systems
• ESSA 2009

OVERALL RATING: -3 (strong reject)
REVIEWER'S CONFIDENCE: 3 (high)
Originality: 2 (poor)
Relevance: 2 (poor)
Technical soundness: 3 (fair)
Significance: 2 (poor)
Quality of presentation: 3 (fair)

The central issue of this paper seems to be the addition of “space” as a concept to a model of non-cooperative games. The issue of “space” is discussed in many simulation studies, even those using game theory or cellular automata so th enovelty of the approach is very limited. The use of “the space” instead of space is a linguistic error that irritates as are several other problems with language. My main point however is the absence of a relation between this work and the issue of the workshop namely social networks. The paper mentions no relationaship between physical positions and social networks and doe snot consider other ties than those with strength 0 (not a neighbour) or 1 (a neighbour), no direction in relationships is acknownledged nor is there a sense of a network (ie consisting of vaiours nodes with relationships, strenghts and directions) that needs analysis.

OVERALL RATING: -1 (weak reject)
REVIEWER'S CONFIDENCE: 4 (expert)
Originality: 3 (fair)
Relevance: 1 (very poor)
Technical soundness: 3 (fair)
Significance: 3 (fair)
Quality of presentation: 4 (good)

The paper deals with evolutionary games and agent based simulation. While mathematically demonstrable under ideal hypotheses, the paper shows, through repeated simulations, that a Nash Equilibrium is reachable even when the assumptions are relaxed and, in particular, agent based simulation can show the step by step dynamics of the system, while mathematical model can’t. Pros: In general the paper is well written and its goals are immediately understandable. It fits the conference topic. Cons: While I agree that agent based simulation can show the dynamics of a system, while mathematical models can’t, I think that the conclusions driven by the authors where quite straightforward. Rather than reaching an equilibrium, I’d refer to the results as a “survival of the fittest”. In fact, through evolution, the agents are substituted by some descendants with a behavior which is like the parental one, plus or minus a mutation. Since it’s obvious that an agent with a better strategy wins over one with e worse one, by repeating the game many times with random agents the ones with worse strategies will of course diminish their energy and eventually disappear. Besides the simulation features many random distributions and probabilities, which are not described in detail, that can highly impact the final results. Were the experiments repeated many times and normalized? This is not clear from the paper. Finally, the graphs are not easily readable and the tag “figure 2” is given to two figures in the paper.

This paper can be accepted only with big modifications. While the proposed goal is interesting, I think that the model is too simple and above all features too many stochastic points. Besides, I think it would be interesting to understand why agents with a sub-optimal strategy survive (s(X+1)/10 and s(X+2)/10) and not just showing that this happens.

• Games spiders play: behavioural variability in territorial disputes. Behavioural Ecology and Sociobiology 3:135-162. 1979.
• Games spiders play II: Resource assessment strategies. Behavioural Ecology and Sociobiology 6:121-128. 1979.
• The consequences of being territorial: spiders, a case of study. American Naturalist 117:871-892. 1981.
• Spider interaction and strategies: communication vs. coercion. 281-315. In Spider communication, Mechanisms and ecological significance. Princeton University Press. 1982.

Over the last twenty years, the role of spatial self-structuring as a template for evolution has drawn much attention. Spatial structure can be an important component of the eco-evolutionary feedback loop: the evolution of a trait (e.g. migration) can shape the local structure of the population, which in turn creates new selective pressures on the evolving trait. As a consequence, the evolution of spatially structured populations often displays very different features from the evolution of well-mixed populations.

I is stable iff:

E(I,I) > E(J,I)

or

E(I,I) = E(J,I) and E(I,J) > E(J,J)

S0 is not ESS against S1:

E(I,I) > E(J,I)
E(S0,S0) > E(S1,S0)
0 > 1 (FALSE)

E(I,I) = E(J,I) and E(I,J) > E(J,J)
E(S0,S0) = E(S1,S0) and E(S0,S1) > E(S1,S1)
0 = 1 (FALSE)

S1 is not ESS against S0:

E(I,I) > E(J,I)
E(S1,S1) > E(S0,S1)
-10 > -1 (FALSE)

E(I,I) = E(J,I) and E(I,J) > E(J,J)
E(S1,S1) = E(S0,S1) and E(S1,S0) > E(S0,S0)
-10 = -1 (FALSE)

Adding Neq (S0.1) to the game, we have:

S0.1 is ESS against S0:

E(I,I) > E(J,I)
E(S0.1,S0.1) > E(S0,S0.1)
-0.1 > -0.1 (FALSE)

E(I,I) = E(J,I) and E(I,J) > E(J,J)
E(S0.1,S0.1) = E(S0,S0.1) and E(S0.1,S0) > E(S0,S0)
-0.1 = -0.1 and +0.1 > 0 (TRUE)

S0.1 is ESS against S1:

E(I,I) > E(J,I)
E(S0.1,S0.1) > E(S1,S0.1)
-0.1 > -0.1 (FALSE)

E(I,I) = E(J,I) and E(I,J) > E(J,J)
E(S0.1,S0.1) = E(S1,S0.1) and E(S0.1,S1) > E(S1,S1)
-0.1 = -0.1 and -1.9 > -10 (TRUE)