Advances in Complex Systems

In this paper, an agent-based model for opinion dynamics on an adaptive coupled random network is proposed. Based on Festinger's idea of "cognitive dissonance", in the proposed model an agent can either make opinion exchange with a neighbor according to the bounded confidence mechanism, or migrate toward another network position in case that the majority of the adjacent agents are beyond the confidence bound. Through numerical simulations, we test how the key factors, such as the interconnectivity of the two communities, the confidence bound or the communal tolerance to diversity, the initial distributions of the opinions, and the level of sense of community, affect the final opinion state of the system. The overall analyses show a general picture of the dynamics of opinions on an adaptive network with community structure. In particular, the results reveal that the clustering of similar agents has a bifurcating function for the opinion dynamics. Given that the inter-communal influence is high, the clustering fosters the global consensus. If the inter-communal influence is weak, the clustering would instead intensify polarization and thus hinder the formation of global consensus. The factors of the communal tolerances and interconnectivity leverage the bifurcating effect.

Learning and adaptation play great role in emergent socio-economic phenomena. Complex dynamics has been previously found in the systems of multiple learning agents interacting via a simple game. Meanwhile, the single agent adaptation is considered trivially stable. We advocate the idea that adopting a more complex model of the individual behavior may result in a more diverse spectrum of macro-level behaviors. We develop an adaptation model based on the reinforcement learning framework extended by an additional processing channel. We scrutiny the dynamics of the single agent adapting to the unknown environment; the agent is biased by novelty seeking, the intrinsic inclination for exploration. We demonstrate that the behavior of the novelty-seeking agent may be inherently unstable. One of the surprising results is that under certain conditions the increase of the novelty-seeking level may cause the agent to switch from the non-rational to the strictly rational behavior. Our results give evidence to the hypothesis that the intrinsic motives of agents should be paid no less attention than the extrinsic ones in the models of complex socio-economic systems.

In this paper, we study trade imbalances between world countries in the period 1960–2011 using a complex-network approach. We show that trade imbalances in absolute value are characterized by a hierarchical arrangement wherein a few developed economies display high clustering and carry an important amount of global-trade imbalances. In contrast, trade imbalances in relative terms show a more fragmented topology, with less concentrated clustering which is particularly high for developing countries. In addition, we find that traditional null random-network models and the gravity model poorly predict the topology of trade imbalance networks. Our main finding is that the evolution of international trade has caused very heterogeneous imbalances in world economies, which may have important consequences for global instability and development.

Human societies are unique in the level of cooperation among non-kin. Evolutionary models explaining this behavior typically assume pure strategies of cooperation and defection. Behavioral experiments, however, demonstrate that humans are typically conditional co-operators who have other-regarding preferences. Building on existing models on the evolution of cooperation and costly punishment, we use a utilitarian formulation of agent decision making to explore conditions that support the emergence of cooperative behavior. Our results indicate that cooperation levels are significantly lower for larger groups in contrast to the original pure strategy model. Here, defection behavior not only diminishes the public good, but also affects the expectations of group members leading conditional co-operators to change their strategies. Hence defection has a more damaging effect when decisions are based on expectations and not only pure strategies.

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

In this paper, we present an agent-based model aiming at supporting decisions about the planning of shopping malls (SM), with a main focus about their environmental impact. The model forecasts the traffic flows induced by SM at the regional level on the whole road network of Canton Ticino (Switzerland), and has been used by the public administration as a support to decide master plan rules. The system has been modeled as an interaction space, a new type of modeling framework that includes both multi-agent systems and cellular automata as a particular case. We define a grammar to specify different agents' trips, and we associate a fuzzy logic based indicator to each one of these specifications. Similarly, we define attractiveness indicators for zones containing economical activities related to agents' trips. The use of these indicators permits to obtain agents' location choices of secondary activities with a great computational efficiency. Simulations include several environmental indicators, like concentrations of nitrogen dioxide, counting changes in a given small part of a road or an estimation of roads' level of service.