Agent-Based and Population-Based Modeling of Trust Dynamics

Jaffry, Syed Waqar; Treur, Jan

doi:10.1007/978-3-642-36815-8_6

Cited by 4 publications

(8 citation statements)

References 21 publications

(23 reference statements)

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“…Agents gain experience as they follow the App recommendation. Trust is calculated following the indirect experience model from Jaffry and Treur (2013) . The proposal is based on the following hypothesis: major trust implies that users will make informed decisions, which results in better crowd management, decreasing in this way the risk contacts .…”

Section: Proposalmentioning

confidence: 99%

“…This species contains particular attributes. Attributes :

(Indicates the level of trust in the App, it dynamically changes following the Dynamic Security-Trust framework as in Choi and Nazareth (2014) and Ngo-Ye, Choi, and Cummings (2018) and the indirect experience dynamic trust model from Jaffry and Treur (2013) ),

(Indicates the source of the last decision made by the user: App recommendation or User knowledge). App: Micro species of User which is in charge to request and show the response from the server to the User.…”

Section: Proposalmentioning

confidence: 99%

“…They select one according to their

level in the App. The

dynamic follows indirect experience the model from Jaffry and Treur (2013) . This model fuses one’s own experience and the influence of the experiences of others.…”

Section: Proposalmentioning

confidence: 99%

“… The stores do not have a monitoring mechanism at the entrance because we are exploring how the tool by itself can prevent multitudes. Trust evolves following the indirect experience model from Jaffry and Treur (2013) . Agents gain experience by using app recommendations.…”

Section: Experimental Workmentioning

confidence: 99%

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Crowd management COVID-19

Polanco¹,

Siller²

2021

Annual Reviews in Control

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Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

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Section: Proposalmentioning

confidence: 99%

“…This species contains particular attributes. Attributes :

Section: Proposalmentioning

confidence: 99%

“…They select one according to their

level in the App. The

dynamic follows indirect experience the model from Jaffry and Treur (2013) . This model fuses one’s own experience and the influence of the experiences of others.…”

Section: Proposalmentioning

confidence: 99%

Section: Experimental Workmentioning

confidence: 99%

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Crowd management COVID-19

Polanco¹,

Siller²

2021

Annual Reviews in Control

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“…In this regard, there exist several studies in literature which perform comparison between these approaches. Research study presented in [22] entirely focuses on trust dynamics and performs variety of experiments using both modeling approaches followed by their comparative analysis. Another comparative study in domain of criminology is presented in [23], which studies the phenomenon of crime displacement.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Trust‐Based Situation Awareness: Comparative Analysis of Agent‐Based and Population‐Based Modeling

Nasar

Jaffry

2018

Complexity

Self Cite

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In recent years, to comprehend and analyze complex systems, multiagent systems modeling and simulation are being widely used across various disciplines. Two major approaches used for multiagent systems modeling and simulation are agent-based modeling (ABM) and population-based modeling (PBM). In multiagent community, it is a silent assumption that both approaches represent similar dynamics for large population size. One of the recent studies from literature has reported similar results for a model of situation awareness spread in multiagent systems. Trust is a significant factor that affects agents’ communication, and consequently it controls spread of situation awareness among agents in a multiagent system. Hence, current work firstly extends the reported model of situation awareness spread from literature, to incorporate interagent trust for both ABM and PBM. Later, these extended models are used for comparative evaluation of both approaches. Various simulation experiments for different population sizes (small and large) as well as population types (homogenous and heterogeneous) are conducted and analyzed. Results of these experiments show that for large and homogeneous population, ABM approximates behavior of PBM, but for even slightly heterogeneous population, these approaches do not produce similar results irrespective of population sizes. Thus, the current study reports that, under some conditions, ABM and PBM produce similar results for trust-based situation awareness spread in multiagent systems, but this assumption does not hold true at large.

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Nonlocal Flocking Dynamics: Learning the Fractional Order of PDEs from Particle Simulations

Mao

Karniadakis

2019

Commun. Appl. Math. Comput.

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Flocking refers to collective behavior of a large number of interacting entities, where the interactions between discrete individuals produce collective motion on the large scale. We employ an agent-based model to describe the microscopic dynamics of each individual in a flock, and use a fractional partial differential equation (fPDE) to model the evolution of macroscopic quantities of interest. The macroscopic models with phenomenological interaction functions are derived by applying the continuum hypothesis to the microscopic model. Instead of specifying the fPDEs with an ad hoc fractional order for nonlocal flocking dynamics, we learn the effective nonlocal influence function in fPDEs directly from particle trajectories generated by the agent-based simulations. We demonstrate how the learning framework is used to connect the discrete agent-based model to the continuum fPDEs in one-and two-dimensional nonlocal flocking dynamics. In particular, a Cucker-Smale particle model is employed to describe the microscale dynamics of each individual, while Euler equations with non-local interaction terms are used to compute the evolution of macroscale quantities. The trajectories generated by the particle simulations mimic the field data of tracking logs that can be obtained experimentally. They can be used to learn the fractional order of the influence function using a Gaussian process regression model implemented with the Bayesian optimization. We show in one-and two-dimensional benchmarks that the numerical solution of the learned Euler equations solved by the finite volume scheme can yield correct density distributions consistent with the collective behavior of the agent-based system solved by the particle method. The proposed method offers new insights on how to scale the discrete agent-based models to the continuum-based PDE models, and could serve as a paradigm on extracting effective governing equations for nonlocal flocking dynamics directly from particle trajectories.

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Agent-Based and Population-Based Modeling of Trust Dynamics

Cited by 4 publications

References 21 publications

Crowd management COVID-19

Crowd management COVID-19

Trust‐Based Situation Awareness: Comparative Analysis of Agent‐Based and Population‐Based Modeling

Nonlocal Flocking Dynamics: Learning the Fractional Order of PDEs from Particle Simulations

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