An Agent-Based Model to Study the Impact of Convex Incentives on Financial Markets

2016 ◽  
pp. 3-13 ◽  
Author(s):  
Annalisa Fabretti ◽  
Tommy Gärling ◽  
Stefano Herzel ◽  
Martin Holmen
Keyword(s):  
Financial Markets ◽  
Agent Based Model ◽  
Agent Based ◽  
The Impact

scholarly journals Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonatan Almagor ◽  
Stefano Picascia
Keyword(s):  
High Rate ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
The Social ◽  
Behavioural Factors ◽  
The Impact ◽  
Different Levels ◽  
User Rate

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.

scholarly journals Examining the Impact of the Walking School Bus With an Agent-Based Model

2014 ◽  
Vol 104 (7) ◽  
pp. 1196-1203 ◽  
Author(s):  
Yong Yang ◽  
Ana Diez-Roux ◽  
Kelly R. Evenson ◽  
Natalie Colabianchi
Keyword(s):  
Agent Based Model ◽  
School Bus ◽  
Agent Based ◽  
Walking School Bus ◽  
The Impact

Analyzing the Influences of Passive Investment Strategies on Financial Markets via Agent-Based Modeling

2008 ◽  
pp. 224-238 ◽  
Author(s):  
Hiroshi Takahashi ◽  
Satoru Takahashi ◽  
Takao Terano
Keyword(s):  
Asset Pricing ◽  
Financial Markets ◽  
Financial Market ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Investment Strategies ◽  
Agent Based Model ◽  
Efficient Market ◽  
Agent Based ◽  
Price Fluctuations

This chapter develops an agent-based model to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. This analysis focuses on the effects of Passive Investment Strategy in a financial market. From the extensive analyses, we have found that (1) Passive Investment Strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (2) under certain assumptions, Passive Investment Strategy and Active Investment Strategy could coexist in a Financial Market.

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

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