scholarly journals Policy Modeling and Applications: State-of-the-Art and Perspectives

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bernardo A. Furtado ◽  
Miguel A. Fuentes ◽  
Claudio J. Tessone
Keyword(s):  
Public Policy ◽  
Evolutionary Game ◽  
Network Models ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Urban Systems ◽  
Policy Makers ◽  
Agent Based ◽  
Corporate Strategies ◽  
Actual Field

The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.

scholarly journals Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

2021 ◽  
Vol 11 (12) ◽  
pp. 5367
Author(s):  
Amirarsalan Rajabi ◽  
Alexander V. Mantzaris ◽  
Ece C. Mutlu ◽  
Ozlem O. Garibay
Keyword(s):  
Disease Model ◽  
Agent Based Modeling ◽  
Contact Tracing ◽  
Test Bed ◽  
Policy Makers ◽  
Agent Based ◽  
Contact Behavior ◽  
Epidemic Curve ◽  
Behavioral Adaptations ◽  
Travel Restrictions

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

scholarly journals Investigating dynamics of COVID-19 spread and containment with agent-based modeling

2020 ◽  
Author(s):  
Amirarsalan Rajabi ◽  
Alexander V. Mantzaris ◽  
Ece C. Mutlu ◽  
Ivan Garibay
Keyword(s):  
Agent Based Modeling ◽  
Contact Tracing ◽  
Model Description ◽  
Test Bed ◽  
Comprehensive Understanding ◽  
Policy Makers ◽  
Agent Based ◽  
Epidemic Curve ◽  
Travel Restrictions ◽  
Travel Restriction

AbstractGovernments, policy makers and officials around the globe are trying to mitigate the effects and progress of the COVID-19 pandemic by making decisions which will save the most lives and impose the least costs. Making these decisions needs a comprehensive understanding about the dynamics by which the disease spreads. In this work, we propose an epidemic agent-based model that simulates the spread of the disease. We show that the model is able to generate an important aspect of the pandemic: multiple waves of infection. A key point in the model description is the aspect of ’fear’ which can govern how agents behave under different conditions. We also show that the model provides an appropriate test-bed to apply different containment strategies and this work presents the results of applying two such strategies: testing, contact tracing, and travel restriction. The results show that while both strategies could result in flattening the epidemic curve and significantly reduce the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

Agent-Based Modeling

2017 ◽  
pp. 36-51
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Linear Behavior ◽  
Complex Adaptive ◽  
Economic Markets ◽  
The Common ◽  
Policy Problems

Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).

Agent-based Modeling for Financial Markets

2018 ◽  
Author(s):  
Giulia Iori ◽  
James Porter
Keyword(s):  
Financial Markets ◽  
Data Gathering ◽  
Simple Zero ◽  
Agent Based Modeling ◽  
Policy Makers ◽  
Agent Based ◽  
Macro Scale ◽  
Learning Capabilities ◽  
Active Data ◽  
Zero Intelligence

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

scholarly journals Road Pricing with Autonomous Links

2005 ◽  
Vol 1932 (1) ◽  
pp. 147-155 ◽  
Author(s):  
Lei Zhang ◽  
David Levinson
Keyword(s):  
Public Policy ◽  
Social Welfare ◽  
Travel Demand ◽  
Road Pricing ◽  
Repeated Game ◽  
Agent Based Modeling ◽  
Pricing Strategies ◽  
Time Model ◽  
Agent Based ◽  
Gains And Losses

This research seeks to examine road pricing on a network of autonomous highway links. “Autonomous” refers to the links’ being competitive and independent and having the objective of maximizing their own profits without regard for either social welfare or the profits of other links. The principal goal of this research is to understand the implications of the adoption of road pricing and privatization on social welfare and the distribution of gains and losses. The specific pricing strategies of autonomous links are evaluated first under the condition of competition for simple networks. An agent-based modeling system is then developed; it integrates an equilibrated travel demand, route choice, and travel time model with a repeated game of autonomous links setting prices to maximize profit. The levels of profit, welfare consequences, and potential cooperative arrangements undertaken by autonomous links are evaluated. By studying how such an economic system may behave under various circumstances, the effectiveness of road pricing and road privatization as public policy can be assessed.

A Unified Framework for Traditional and Agent-Based Social Network Modeling

2014 ◽  
pp. 184-199 ◽  
Author(s):  
Enrico Franchi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Network ◽  
Conceptual Framework ◽  
Network Models ◽  
General Purpose ◽  
Agent Based Modeling ◽  
Unified Framework ◽  
Modeling Tool ◽  
Agent Based ◽  
Meta Model ◽  
Social Network Modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms.

Creativity in a theory of entrepreneurship

2019 ◽  
Vol 8 (4) ◽  
pp. 442-469 ◽  
Author(s):  
James Lee Caton
Keyword(s):  
Computational Simulation ◽  
Building Blocks ◽  
Agent Based Modeling ◽  
Competitive System ◽  
Policy Makers ◽  
Content Type ◽  
Agent Based ◽  
The Social ◽  
New Knowledge ◽  
The Individual

Purpose The purpose of this paper is to integrate a detailed theory of perception and action with a theory of entrepreneurship. It considers how new knowledge is developed by entrepreneurs and how the level of creativity is regulated by a competitive system. It also shows how new knowledge may create value for the innovator as well as for other entrepreneurs in the system. Design/methodology/approach The theory builds on existing literature on creativity and entrepreneurship. It considers how transformation of mental technologies occurs at the individual and system levels, and how this transformation influences value creation. Findings Under a competitive system, the level of creativity is regulated by the need for new ways of doing things. Periods of crisis wherein old means of coordination begin to fail often precipitate an increase in creativity, whereas a lack of crisis often allows the system to settle to a stable equilibrium with lower levels of creativity. Research limitations/implications The combination of methodology and methods facilitates a description of discrete building blocks that guide perception and enable creativity. This framing enables consideration of how a changing set of knowledge interacts with a system of prices. Practical implications Policy makers must take care not to encumber markets with costs that unnecessarily constrain creativity, as experimentation makes the economic system robust to shocks. Social implications This work provides a framing of cognition that allows for a linking of agent understanding that permits explicit description of coordination between agents. It relates perception and ends of the individual to constraints enforced by the social system. Originality/value As far as the author is concerned, no other work ties together a robust framing of cognition with computational simulation of market processes. This research deepens understanding in multiple fields, most prominently for agent-based modeling and entrepreneurship.

scholarly journals Estimated Time to Restoration of Hurricane Sandy in a Future Climate

2020 ◽  
Vol 12 (16) ◽  
pp. 6502
Author(s):  
Tara C. Walsh ◽  
David W. Wanik ◽  
Emmanouil N. Anagnostou ◽  
Jonathan E. Mellor
Keyword(s):  
Critical Infrastructure ◽  
Hurricane Sandy ◽  
Agent Based Modeling ◽  
Power Outage ◽  
Policy Makers ◽  
Agent Based ◽  
Emergency Managers ◽  
Past Experiences ◽  
The Impact ◽  
The Relationship

Power outage restoration following extreme storms is a complicated process that couples engineering processes and human decisions. Emergency managers typically rely on past experiences and have limited access to computer simulations to aid in decision-making. Climate scientists predict that although hurricane frequency may decrease, the intensity of storms may increase. Increased damage from hurricanes will result in new restoration challenges that emergency managers may not have experience solving. Our study uses agent-based modeling (ABM) to determine how restoration might have been impacted for 30 different scenarios of Hurricane Sandy for a climate in 2112 (Sandy2112). These Sandy2112 scenarios were obtained from a previous study that modeled how outages from Hurricane Sandy in 2012 might have been affected in the future as climate change intensified both wind and precipitation hazards. As the number of outages increases, so does the expected estimated time to restoration for each storm. The impact of increasing crews is also studied to determine the relationship between the number of crews and outage durations (or restoration curves). Both the number of outages and the number of crews impact the variability in time to restoration. Our results can help emergency managers and policy makers plan for future hurricanes that are likely to become stronger and more impactful to critical infrastructure.

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