Narrow Results

We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting "dominated" by a leader's decision. The interplay of both kind of agents gives rise to complex price dynamics that is consistent with the main stylized facts of financial time series. The present model incorporates many features of other known models and is meant to be the first step toward the construction of an agent-based model that uses more realistic markets rules, strategies, and information structures.

Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products dramatically depend on consumer heterogeneity and social interaction pressure. Furthermore, the relation between the resulting market shares and social interaction is robust with respect to a wide range of variation in the parameter values and the type of topology.

In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment is of interest to large numbers of organizations and people. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or it is more open and above board, such as researchers who want to make large groups of people aware of helpful innovations, what is at stake is often significant.

In this paper, we review opinion dynamics including the extensions of many classical models as well as some new models that deepen understanding. For example, we look at models that track the evolution of an individual’s power, that include noise, and that feature sequentially dependent topics, to name a few.

While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas.

It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

Though numerous studies demonstrate the importance of social influence in deciding individual decision-making process in networks, little has been done to explore its impact on players’ behavioral patterns in evolutionary prisoner’s dilemma games (PDGs). This study investigates how social influenced strategy updating rules may affect the final equilibrium of game dynamics. The results show that weak social influence usually inhibits cooperation, while strong social influence has a mediating effect. The impacts of network structure and the existence of rebels in social influence scenarios are also tested. The paper provides a comprehensive interpretation on social influence effects on evolutionary PDGs in networks.

The introduction of extortion strategy has attracted much attention since it dominates any evolutionary opponent in iterated prisoner’s dilemma games. Despite several studies argue that extortion is difficult to survive under strategy imitation and birth–death updating rules in well-mixed populations, it has recently been proven that a myopic best response rule facilitate the evolution of cooperation and extortion. However, such updating rules require a strong assumption of complete knowledge of all players, which is unlikely to hold in social networks in reality. To solve this problem, we introduce the concept of social influence into the model to limit players’ knowledge within their neighborhood. It turns out that this myopia initiated by social influence prevents players from observing superior strategies and therefore enables cooperators and extortioners to be evolutionarily stable. We also suggest that heterogeneous networks contribute to the evolution of cooperation and extortion under such social influence.

We consider a model of socially interacting individuals that make a binary choice in a context of positive additive endogenous externalities. It encompasses as particular cases several models from the sociology and economics literature. We extend previous results to the case of a general distribution of idiosyncratic preferences, called here Idiosyncratic Willingnesses to Pay (IWP). When j, the ratio of the social influence strength to the standard deviation of the IWP distribution, is small enough, the inverse demand is a classical monotonic (decreasing) function of the adoption rate. However, even if the IWP distribution is mono-modal, there is a critical value of j above which the inverse demand is non-monotonic. Thus, depending on the price, there are either one or several equilibria.

Beyond this first result, we exhibit the generic properties of the boundaries limiting the regions where the system presents different types of equilibria (unique or multiple). These properties are shown to depend only on qualitative features of the IWP distribution: modality (number of maxima), smoothness and type of support (compact or infinite). The main results are summarized as phase diagrams in the space of the model parameters, on which the regions of multiple equilibria are precisely delimited. We also discuss the links between the model and the random field Ising model studied in the physics literature.

In this paper we investigate the degree to which two social influences, namely imitation and coordinated consumption, effectuate inequalities in the motion picture industry. We develop an agent-based model based on micro movie visitors' decision-making that generates the observed macro market outcomes. The simulation model makes use of the findings of an empirical survey amongst 1112 cinema visitors. We find that social influences explain market inequalities and that the impact of coordinated consumption on market inequalities is stronger than the impact of imitation.

Despite the increasing diffusion of the Internet technology, TV remains the principal medium of communication. People's perceptions, knowledge, beliefs and opinions about matters of fact get (in)formed through the information reported on by the media.

However, a single source of information (and consensus) could be a potential cause of anomalies in the structure and evolution of a society.

Hence, as the information available (and the way it is reported) is fundamental for our perceptions and opinions, the definition of conditions allowing for a good information to be disseminated is a pressing challenge. In this paper starting from a report on the last Italian political campaign in 2008, we derive a socio-cognitive computational model of opinion dynamics where agents get informed by different sources of information. Then, a what-if analysis, performed through simulations on the model's parameters space, is shown. In particular, the scenario implemented includes three main streams of information acquisition, differing in both the contents and the perceived reliability of the messages spread. Agents' internal opinion is updated either by accessing one of the information sources, namely media and experts, or by exchanging information with one another. They are also endowed with cognitive mechanisms to accept, reject or partially consider the acquired information.

This paper introduces a connectionist Agent-Based Model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes [D. Van Rooy, F. Van Overwalle, T. Vanhoomissen, C. Labiouse and R. French, Psychol. Rev. 110, 536 (2003)]. At the macro-level, these individual networks are combined into a "community of networks" so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them [B. Hazlehurst, and E. Hutchins, Lang. Cogn. Process. 13, 373 (1998)]. The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. In a extensive research program, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes.

In this paper, we present a new agent-based model for the simulation of tax compliance and tax evasion behavior (SIMULFIS). The main novelties of the model are the introduction of a "behavioral filter approach" to model tax decisions, the combination of a set of different mechanisms to produce tax compliance (namely rational choice, normative commitments and social influence), and the use of the concept of "fraud opportunity use rate" (FOUR) as the main behavioral outcome. After describing the model in detail, we display the main behavioral and economic results of 1,920 simulations calibrated for the Spanish case and designed to test for the internal validity of SIMULFIS. The behavioral outcomes show that scenarios with strict rational agents strongly overestimate tax evasion, while the introduction of social influence and normative commitments allows to generate more plausible compliance levels under certain deterrence conditions. Interestingly, the relative effect of social influence is shown to be ambivalent: it optimizes compliance under low and middle deterrence conditions, but not when deterrence is made harder. Finally, SIMULFIS economic outcomes are broadly in line with theoretical expectations, thus supporting the reliability of the model.

We study a system in which N agents have to decide between two strategies θ_i(i ∈ 1 … N), for defection or cooperation, when interacting with other n agents (either spatial neighbors or randomly chosen ones). After each round, they update their strategy responding nonlinearly to two different information sources: (i) the payoff a_i(θ_i, f_i) received from the strategic interaction with their n counterparts, (ii) the fraction f_i of cooperators in this interaction. For the latter response, we assume social herding, i.e., agents adopt their strategy based on the frequencies of the different strategies in their neighborhood, without taking into account the consequences of this decision. We note that f_i already determines the payoff, so there is no additional information assumed. A parameter ζ defines to what level agents take the two different information sources into account. For the strategic interaction, we assume a Prisoner's Dilemma game, i.e., one in which defection is the evolutionary stable strategy. However, if the additional dimension of social herding is taken into account, we find instead a stable outcome where cooperators are the majority. By means of agent-based computer simulations and analytical investigations, we evaluate the critical conditions for this transition toward cooperation. We find that, in addition to a high degree of social herding, there has to be a nonlinear response to the fraction of cooperators. We argue that the transition to cooperation in our model is based on less information, i.e., on agents which are not informed about the payoff matrix, and therefore rely on just observing the strategy of others, to adopt it. By designing the right mechanisms to respond to this information, the transition to cooperation can be remarkably enhanced. Our results are obtained in an evolutionary PD game with fixed payoffs and a fixed four-player neighborhood, where agents follow a stochastic better response dynamics.

We expect that democracy enables us to utilize collective intelligence such that our collective decisions build and enhance social welfare, and such that we accept their distributive and normative consequences. Collective decisions are produced by voting procedures which aggregate individual preferences and judgments. Before and after, individual preferences and judgments change as their underlying attitudes, values, and opinions change through discussion and deliberation. In large groups, these dynamics naturally go beyond the scope of the individual and consequently might show unexpected self-driven macroscopic systems dynamics following socio-physical laws. On the other hand, aggregated information and preferences as communicated through media, polls, political parties, or interest groups, also play a large role in the individual opinion formation process. Further on, actors are also capable of strategic opinion formation in the light of a pending referendum, election or other collective decision. Opinion dynamics and collective decision should thus not only be tackled by social choice, game theory, political and social psychology, but also from a systems dynamics and sociophysics perspective.

The advantages of groups over individuals in complex decision-making have long interested scientists across disciplinary divisions. Averaging over a collection of individual judgments proves a reliable strategy for aggregating information, particularly in diverse groups in which statistically independent beliefs fall on both sides of the truth and contradictory biases are cancelled out. Social influence, some have said, narrows variation in individual opinions and undermines this wisdom-of-crowds effect in continuous estimation tasks. Researchers, however, neglected to study social-influence effects on voting in discrete choice tasks. Using agent-based simulation, we show that under voting — the most widespread social decision rule — social influence contributes to information aggregation and thus strengthens collective judgment. Adding to our knowledge about complex systems comprised of adaptive agents, this finding has important ramifications for the design of collective decision-making in both public administration and private firms.

Polarization between groups is a major topic of contemporary societal debate and research. Formal models of opinion dynamics try to explain how intergroup polarization arises from simple first principles of social interaction. In existing models, intergroup attitudes affect social influence in the form of homophily or xenophobia, fixed tendencies of individuals to be more open to influence from ingroup members or distance themselves from attitudes of outgroup members. These models generate polarization between groups, but they neglect a central insight from empirical research. Intergroup attitudes are themselves subject to social influence in interactions with both in- and outgroup members. A model is proposed in which the attitude which is subject to social influence is also an intergroup attitude. It affects in turn the influence process itself. Furthermore, it is shown how this changes model predictions about process and conditions of polarization between groups. More complex patterns of intergroup relations emerge than in a model with fixed xenophobia. Especially, a renegade minority (‘outgroup lovers’) is found to have a key role in avoiding mutually negative intergroup relations and even elicit reversed polarization, resulting in a majority of individuals developing a negative attitude towards their ingroup and a positive one for the outgroup.

Despite extensive studies on consumer behavior and decision making, the social influence of consumers on each other has not been widely investigated. To incorporate such interactions, in this study, we propose and apply an agent-based simulation model where consumers are defined as agents. The purchase behavior of each agent is characterized as a function based on the concept of the black-box model for consumer behavior. In particular, we investigate the effect of consumers’ social network and its interaction with the marketing mix parameters (4Ps). A case study of household appliances in a local market is used to demonstrate how the dynamics of preferences between domestic and foreign brands occurs. The simulation model is used to examine the effect of eight scenarios related to these interactions. The obtained results are compared and the most important factors are determined as product features and price.

Most of the studies investigating innovation adoption behavior focus on technological products and services. Considering the significant gap in the conceptualization and operationalization of social innovation adoption behavior in the literature, the aim of this study is to provide an insight to the adoption behavior of social innovations. The effects of perceived usefulness, perceived enjoyment, perceived ease of use and need for approval are investigated within the context of time bank activities in Turkey. The result of the analysis supported the significant effect of all independent variables with a higher impact of perceived enjoyment.

This study investigated the factors that influence Ghanaian entrepreneurs to adopt e-commerce. Cross-sectional data was gathered from 520 entrepreneurs in the most populous and industrious regions in Ghana. The unified theory of acceptance and use of technology (UTAUT) was employed to effectively understand the unexplored phenomenon of e-commerce adoption among Ghanaian entrepreneurs. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to test the hypothesized relationships. The findings indicate that performance expectancy, effort expectancy, and social influence (SI) positively and significantly influenced the behavioral intention (BI) to adopt e-commerce. Facilitating conditions (FC) and BI had a significant positive relationship with the adoption of e-commerce.

The research paper addresses the adoption of the mobile application for ordering from the restaurant by the user with their value during the pandemic period. The model used here is the UTAUT (Unified Theory of Acceptance and Use of Technology) model, the research model has six determinants to address intention to use — performance expectancy, perceived risk, price saving, social influence, effort expectancy, and facilitating condition. The data was collected among college students from South India, who are regular in using the mobile application for the ordering. The sample size of 120 respondents was collected and analysed using SEM (Structured Equation Modeling). The SEM results give that the drivers of intention to use the mobile application are social influence and price saving that are significantly related, perceived risk is significant with the social influence, social influence significantly impacts price saving and performance expectancy impacts price saving construct.

The research attempts to determine the inclination toward embracing e-commerce, focusing on the satisfaction of low- and middle-income consumers with online services and their ability to navigate a secure online platform. We examine our hypothesis with survey technique. The questionnaire was adapted from prior studies. The research was carried out through empirical methods, utilizing a convenience sample of 290 participants from various locations in Barishal, Chattogram, Dhaka, Khulna, Rajshahi, Rangpur, Mymensingh, and Sylhet, from Bangladesh. SMARTPLS was used for analysis of the gathered data. The findings demonstrated that the desire to utilize e-commerce is strongly and positively predicted by perceived risk, social influence, and facilitating aspects. This study addresses the gap by identifying and understanding these three aspects together, beyond contextual barriers, that influence e-commerce adoption among low- and middle-income consumers in Bangladesh. The study contributes to the understanding of e-commerce adoption in emerging markets by providing a comprehensive, theoretically grounded model that identifies key institutional and contextual elements influencing online shopping behavior. The insights gained offer valuable implications for policymakers and e-commerce platforms aiming to enhance user engagement in online shopping. However, the lack of thorough study that addresses problems outside contextual imperatives limits our knowledge of what motivates e-commerce in Bangladesh. This study examines a comprehensive, theoretically developed model that find the pertinent institutional and contextual elements that might lead to the uptake of online shopping in underdeveloped countries.