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Conditional dissociation, i.e. the option to leave an interacting partner in response to his behaviour, is a mechanism that has been shown to promote cooperation in several settings, but the fundamental features that make conditional dissociation work in this way are not yet fully understood. This paper identifies some of the key conditions that make conditional dissociation lead to high levels of cooperation, explains how this mechanism can support the evolutionary coexistence of cooperative and non-cooperative behaviour typically observed in nature, and provides an analytical formula to estimate the expected degree of cooperation thus achieved. Our model involves a population of individuals who are paired to play an iterated prisoner's dilemma. All individuals share the same capacity to react to the action previously chosen by their partner and, without any other a priori constraint or exclusion, they may use any behavioural rule that is compatible with this capacity. The dynamic evolution of the population eventually enters either a non-cooperative or a partially cooperative regime, depending mainly on the expected lifetime of individuals. Whenever the partially cooperative regime materializes, the cornerstone of its long-run stability is the coexistence of defectors and "Out-for-Tat" strategists, the latter being those who start cooperating and respond to defection by merely leaving. We find, therefore, that conditional dissociation is the essential disciplinary device supporting cooperation, whilst other conditional strategies (such as Tit-for-Tat) remain present only in small population shares. These conclusions are obtained both by extensive numerical simulations and through analytical mean-field methods that approximate the stochastic simulation dynamics and deliver accurate predictions for general parameter configurations.

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We implement a diffusion model for an innovative product in a market with a structure of social relationships. Diffusion is described with a percolation approach in the price space. Percolation shows a phase transition from a diffusion to a no-diffusion regime. This has strong implications for market demand and pricing. We study the effect of network structure on market diffusion efficiency by considering a number of cases, such as one-dimensional and two-dimensional lattices, small worlds, Poisson networks and Scale-free networks. We consider two measures of diffusion efficiency: the size of diffusion and the diffusion time-length. We find that network connectivity "spreading" is the most important factor for the size of diffusion. Clustering is ineffective. This means that societies with higher dimensionality are better markets for diffusion. This result is most evident for the size of diffusion, while a short average path-length is more important for the speed of diffusion. Endogenous learning curves shift the percolation threshold to higher prices, and constitute an endogenous mechanism of price discrimination. The best market strategy of innovation diffusion is to start with high price and allow for a learning curve. 1

Agent-based modeling is being increasingly used to simulate socio-techno-ecosystems that involve social dynamics. Humans face constraints that they sometimes wish to challenge, and when they do so, they often trigger changes at the scale of the social group too. Including such adaptation dynamics explicitly in our models would allow simulation of the endogenous emergence of rule changes. This paper discusses such an approach in an institutional framework and develops a sequence that allows modeling of endogenous rule changes. Parts of this sequence are implemented in a NetLogo KISS model to provide some illustrative results.