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The development of information and communication technologies and computing power leads to the emergence of additional opportunities for modeling political processes. In the past decades, mathematical models have been developed mainly in a game-theoretic setting; today we witness an expanding stream of research applying agent-based (multi-agent) approach. This trend is quite natural. There have been changes in political participation and in the forms of collective interaction of individuals and groups, induced by digital technologies. Researchers have developed theoretical approaches to political participation, focusing on the network interaction and implementing the "bottom-up" logic that infers the macro-properties of the system from the characteristics and interactions of individual agents. Thus, the theoretical foundations for an agent-based modeling, most promising in its network version, have been developed. This approach, however, required a more complex description of the individual motivation and decision making in comparison to the dominant game-theoretic paradigm. One of the key points is that motivation is considered to be linked to the network position of agents, since the individual is guided by the actions of her neighbors. Thus, the course of the political process is determined not only by the properties and decisions of its participants, but also by the type of network architecture that connects them. Within this research framework, a computational experiment, assuming a controlled variation of parameters, plays a special role. Two main strategies of such an experiment are considered: the grid search and the Monte Carlo method. The prospects of agentbased modeling in its network form are related to the study of the dynamical political processes, taking into account the structures of trust and social capital, as well as the resources and mechanisms of collective action.

AI designers endeavour to improve 'autonomy' in artificial intelligent devices, as recent developments show. This chapter firstly argues against attributing metaphysical attitudes to AI and, simultaneously, in favor of improving autonomous AI which has been enabled to respect autonomy in human agents. This seems to be the only responsible way of making further advances in the field of autonomous social AI. Let us examine what is meant by claims such as designing our artificial alter egos and sharing moral selves with artificial humanoid devices as well as providing autonomous AI with an ethical framework modelled upon the core aspects of moral selfhood, e.g., making decisions which are based on autonomous law-giving, in Kantian terms. ; National Science Centre (NCN, Kraków, Poland), grant OPUS 9, no 2015/17/B/HS1/02381. ; 6 ; 2 ; 169 ; 208 ; 7 ; Filozofia Publiczna i Edukacja Demokratyczna

Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure. ; BMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacities