Arshad Muhammad

Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms

People live in society adhering to different type of norms. Some of these norms are unpopular. Sometimes, for the overall societal good, it is necessary to oppose and possibly avert the unpopular norms. To achieve this goal, it is important to know the conditions which enable persistence of unpopular norms and models that support possible aversion of these. This study attempts to elaborate the conditions and reasons behind the emergence, spreading and aversion of unpopular norms in a society using theory-driven agent-based simulation. The simulation results revealed that, in addition to agents actively participating in averting the unpopular norm, incorporating a rational decision making model for normal agents is necessary to achieve a dominant norm aversion.

Exploration and Exploitation Measurement in Swarm-Based Metaheuristic Algorithms: An Empirical Analysis

Swarm-based metaheuristics, inspired from intelligent social behaviors in nature, have achieved wider acceptance among researchers as compared to other population-based methods. The success of any swarm-based algorithm highly depends upon the mechanism of social interaction which maintains the balance between exploration and exploitation. This research examines these two significant cornerstones of top five swarm-based metaheuristics using diversity measurement. The results show that ACO and FA maintained balance between exploration and exploitation throughout iterations thus achieved better results as compared to counterparts taken in this study.

Impact of Mobility Mode on Innovation Dissemination: An Agent-Based Simulation Modeling.

In this paper, an agent-based model of information/innovation dissimulation and adoption, motivated by seminal model from Centola and Macy is presented. The four factors contributing to complex contagion, namely, strategic complementarity, credibility, legitimacy, and emotional contagion are considered. A parameterized mechanism of relating credibility with legitimacy in time and spatial domains is presented. Innovation dissemination in different mobility modes is studied using a proximity-based regular network. The simulation results reveal that the late adopters are affected by the early adopters, but only when the mobility model is closer to human mobility (a planned, scheduled and repeated mobility). Early adopters do not affect late adopters if all agents are stationary or acquiring random walk mobility. Also, with an increase in the percentage of early adopters, the number of people adopting an innovation increases. All the other varying factors, such as interaction radius, threshold values, etc. do not have substantial impact.

A Demonstration of Simulation Modeling for SIoV Recommendations System

This paper demonstrates how to model a simulation of Social Internet of Things (SIoV) based recommendation system. An agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. We demonstrate how we model our simulation using NetLogo.

Agent-Based Model of Smart Social Networking-Driven Recommendations System for Internet of Vehicles

Social aspects of connectivity and information dispersion are often ignored while weighing the potential of Internet of Things (IoT). Assuming a more commonly acceptable standardization of Big Data generated by Internet of Vehicles (IoV), the social dimensions enabling its fruitful usage; emerging as Social IoV (SIoV); remains a challenge. In this paper, an agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves as a result of incremental interactions, recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.

Agent-based Modeling of a Self-Organized Food Safety System

“The wisdom of crowds” is often observed in social discourses and activities around us. The manifestations of it are, however, so intrinsically embedded and behaviorally accepted that an elaboration of a social phenomenon evidencing such wisdom is often considered a discovery; or at least an astonishing fact. One such scenario is explored here, namely, the conceptualization and modeling of a food safety system—a system directly related to social cognition. The first contribution of this paper is the re-evaluation of Knowles’s model towards a more conscious understanding of “the wisdom of crowds” effects on inspection and consumption behaviors. The second contribution is augmenting the model with social networking capabilities, which acts as a medium to spread information about stores and help consumers find uncontaminated stores. Simulation results revealed that stores respecting social cognition improve the effectiveness of the food safety system for consumers as well as for the stores. Simulation findings also revealed that active societies have the capability to self-organize effectively, even if they lack regulatory obligations.

Socially aware peers for futuristic web of things

Research in Internet of Things (IoTs) have surpassed the notions of connectivity and smartness, and entered into an era of sociable objects. A model based on socially-influenced interactions for “social objects” is presented to demonstrate its application and strength. Adopting an agent-based modeling method and simulation, a comparative study of competitive vs. cooperative behavior of peers in shared services scenario is presented. The simulation results reveal that social ability of the peers could result in substantial increase in quality of interactions between social objects.

Modeling human factors influencing herding during evacuation

Purpose It has been witnessed that many incidents of crowd evacuation have resulted in catastrophic results, claiming lives of hundreds of people. Most of these incidents were a result of localized herding that eventually turned into global panic. Many crowd evacuation models have been proposed with different aspects of interests. The purpose of this paper is to attempt to bring together many of these aspects to study evacuation dynamics. Design/methodology/approach The proposed agent-based model, in a hypothetical physical environment, uses perception maps for routing decisions which are constructed from agents’ personal observations of the surroundings as well as information gathered through distant communication. Communication is governed by a trust model which measures the authenticity of the information being shared. Agents are of two types; emotional and rational. The trust model is combined with a game-theoretic model to resolve conflict of agents’ own type with that of types of agents in the neighborhood. Findings Evacuation dynamics in different environmental and exit strategies are evaluated on the basis of reduced herding and evacuation time. Using this integrated information sharing model, agents gain an overall view of the environment, sufficient to select the optimal path towards exits with respect to reduced herding and evacuation time. Originality/value The proposed model has been formulated and established using an agent-based simulation integrating important modeling aspects. The paper helps in understanding the interplay between technological and humanistic aspects in smart and pervasive environments.

Mobile Sensing for Data-Driven Mobility Modeling

The use of mobile sensed location data for realistic human track generation is privacy sensitive. People are unlikely to share their private mobile phone data if their tracks were to be simulated. However, the ability to realistically generate human mobility in computer simulations is critical for advances in many domains, including urban planning, emergency handling, and epidemiology studies. In this paper, we present a data-driven mobility model to generate human spatial and temporal movement patterns on a real map applied to an agent based setting. We address the privacy aspect by considering collective participant transitions between semantic locations, defined in a privacy preserving way. Our modeling approach considers three cases which decreasingly use real data to assess the value in generating realistic mobility, considering data of 89 participants over 6079 days. First, we consider a dynamic case which uses data on a half-hourly basis. Second, we consider a data-driven case without time of day dynamics. Finally, we consider a homogeneous case where the transitions between locations are uniform, random, and not data-driven. Overall, we find the dynamic data-driven case best generates the semantic transitions of previously unseen participant data.