An agent-based model of repeated decision making under risk: modeling the role of alternate reference points and risk behavior on long-run outcomes

AbstractA contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

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Role of Heterogeneous Behavioral Factors in an Agent-Based Model of Crop Choice and Groundwater Irrigation

Journal of Water Resources Planning and Management ◽

10.1061/(asce)wr.1943-5452.0001033 ◽

2019 ◽

Vol 145 (2) ◽

pp. 04018100 ◽

Cited By ~ 1

Author(s):

Yao Hu ◽

Steve Beattie

Keyword(s):

Behavioral Factors ◽

Agent Based Model ◽

Agent Based ◽

Crop Choice ◽

Groundwater Irrigation

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The effects of reward system on bank credit losses – an agent-based model

Managerial Finance ◽

10.1108/mf-07-2014-0209 ◽

2015 ◽

Vol 41 (9) ◽

pp. 908-924

Author(s):

Sara Jonsson

Keyword(s):

Reward System ◽

Bank Credit ◽

Reward Systems ◽

Risk Modeling ◽

Agent Based Model ◽

Credit Risk Management ◽

Content Type ◽

Agent Based ◽

Loan Officer ◽

Credit Losses

Purpose – The purpose of this paper is to investigate how the design of loan officer reward systems affects bank credit losses caused by commercial clients. Design/methodology/approach – This paper uses an agent-based model to investigate how the design of reward systems affects bank credit losses. Two different systems are compared: competitive and a cooperative. The model is designed according to the theoretically derived assumption that a cooperative reward system will make agents more likely to share knowledge with each other in the processes of granting and monitoring credit. Findings – The results show that a cooperative reward system have potential to reduce bank credit losses. The reduction of errors in evaluating company’s probability of default thus mitigates variations induced by variations in industry, region, and firm-specific returns. Practical implications – The findings imply that reward system design should be considered in credit risk management. Further, managerial issues (e.g. reward systems) should be considered in risk modeling. Originality/value – The results presented in this paper provide evidence to the value of considering the downside (e.g. loss) when designing reward systems in banks.

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Lattice-based microenvironmental uncertainty driven phenotypic decision-making: a comparison with Notch-Delta-Jagged signaling

10.1101/2021.11.16.468748 ◽

2021 ◽

Author(s):

Aditi Ajith Pujar ◽

Arnab Barua ◽

Divyoj Singh ◽

Ushasi Roy ◽

Mohit Kumar Jolly ◽

...

Keyword(s):

Decision Making ◽

Variational Principle ◽

Signaling Pathway ◽

Biological System ◽

Single Cells ◽

Tissue Level ◽

Spatiotemporal Patterns ◽

Complex Environment ◽

Agent Based

Phenotypic decision-making is a process of determining important phenotypes in accordance with the available microenvironmental information. Although phenotypic decision at the level of a single cell has been precisely studied, but the knowledge is still imperceptible at the multicellular level. How cells sense their environment and adapt? How single cells change their phenotype in a multicellular complex environment (without knowing the interactions among the cells), is still a rheotorical question. To unravel the fragmental story of multicellular decision-making, Least microEnvironmental Uncertainty Principle (LEUP) was refined and applied in this context. To address this set of questions, we use variational principle to grasp the role of sensitivity, build a LEUP driven agent-based model on a lattice which solely hinges on microenvironmental information and investigate the parallels in a well-known biological system, viz., Notch-Delta-Jagged signaling pathway. The analyses of this model led us to interesting spatiotemporal patterns in a population of cells, responsive to the sensitivity parameter and the radius of interaction. This resembles the tissue-level pattern of a population of cells interacting via Notch-Delta-Jagged signaling pathway in some parameter regimes.

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