Agent-Based Models for Economic Policy Design

2018 ◽  
Author(s):  
Frank Westerhoff ◽  
Reiner Franke
Keyword(s):  
Economic Policy ◽  
Policy Design ◽  
Market Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Technical And Fundamental Analysis ◽  
Model Dynamics ◽  
Goods Market ◽  
Stabilization Policies ◽  
The Impact

With the help of two examples, this chapter illustrates the usefulness of agent-based models as tools for economic policy design. The first example applies a financial market model in which the order flow of speculators, relying on technical and fundamental analysis, generates intricate price dynamics. The second example applies a Keynesian-type goods market model in which the investment behavior of firms, relying on extrapolative and regressive predictors, generates complex business cycles. It adds a central authority to these two setups and explores the impact of simple intervention strategies on the model dynamics. On the basis of these experiments, the chapter concludes that agent-based models may help us understand how markets function and evaluate the effectiveness of various stabilization policies.

scholarly journals Agent-based Models for Economic Policy Design

2010 ◽  
Vol 37 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Herbert Dawid ◽  
Michael Neugart
Keyword(s):  
Economic Policy ◽  
Policy Design ◽  
Agent Based Models ◽  
Agent Based

MACROECONOMIC STABILIZATION AND LONG-TERM GROWTH: THE ROLE OF POLICY DESIGN

2019 ◽  
pp. 1-46 ◽  
Author(s):  
Philipp Harting
Keyword(s):  
Policy Design ◽  
Theoretical Perspective ◽  
Long Term Effects ◽  
Agent Based ◽  
Stabilization Policies ◽  
Endogenous Business Cycles ◽  
The Business Cycle ◽  
Fiscal Stabilization

Do fiscal stabilization policies affect the long-term growth of the economy? If so, are the long-term effects growth enhancing or growth reducing? When addressing these questions from a theoretical perspective, the literature has typically emphasized the importance of structural aspects such as the modeling approach of endogenous technological change while paying less attention to an elaborate design of the considered fiscal stabilization policies. This paper uses an agent-based macroeconomic model that generates endogenous business cycles to emphasize the role of the policy design for long-term growth effects of stabilization policies. By comparing a demand-oriented consumption policy and two different investment subsidizing policies, it can be shown that these policies are successful in smoothing the business cycle but differ in terms of their effects on economic long-term growth. This highlights the importance of policy design for the analysis of long-term effects of stabilization policies.

scholarly journals Filling the Gaps: Vacancy Chains and Agent-Based Models

2020 ◽  
Author(s):  
Radu Andrei Pârvulescu
Keyword(s):  
Markov Chain ◽  
Socialist Country ◽  
Social Revolution ◽  
Agent Based Models ◽  
Agent Based ◽  
Equilibrium Dynamics ◽  
Chain Analysis ◽  
Vacancy Chains ◽  
The Impact ◽  
Scholarly Attention

Vacancy-chain analysis (VCA), a method for tracing the flows of resources such as jobs or housing, has faded from scholarly attention. This is unfortunate, because VCA is often superior to markets, auctions, or games, the more popular metaphors-cum-models of resource allocation. This paper aims to revive VCA by casting it in terms of agent-based models (ABMs). I first review and note the limitations of the Markov-chain version VCA (or MC-VCA), and then introduce an agent-based approach to vacancy chain systems, the ABM-VCA, which features the innovation of treating both resources/positions and opportunities as agents. I show that ABM-VCA can replicate MC-VCA (since the former is an MCMC estimator of the latter) and then illustrate the usefulness of ABM-VCA to empirically study off-equilibrium dynamics by using it to assessing the impact of social revolution on the judiciary of a post-socialist country. I conclude by noting the methodological possibilities opened up by ABM-VCA, such as the potential to simulating fields and ecologies. A Python implementation of ABM-VCA is available at https://github.com/r-parvulescu/abm-vca.

Agent-Based Modeling of Flood Insurance Futures

2020 ◽  
Author(s):  
Linda Geaves
Keyword(s):  
Autonomous Agents ◽  
Insurance Industry ◽  
Model Development ◽  
Policy Instruments ◽  
Flood Insurance ◽  
Agent Based Models ◽  
Online Data ◽  
Agent Based ◽  
Artificial Intelligence Technology ◽  
The Impact

Agent-based models have facilitated greater understanding of flood insurance futures, and will continue to advance this field as modeling technology develops further. As the pressures of climate-change increase and global populations grow, the insurance industry will be required to adapt to a less predictable operating environment. Complicating the future of flood insurance is the role flood insurance plays within a state, as well as how insurers impact the interests of other stakeholders, such as mortgage providers, property developers, and householders. As such, flood insurance is inextricably linked with the politics, economy, and social welfare of a state, and can be considered as part of a complex system of changing environments and diverse stakeholders. Agent-based models are capable of modeling complex systems, and, as such, have utility for flood insurance systems. These models can be considered as a platform in which the actions of autonomous agents, both individuals and collectives, are simulated. Cellular automata are the lowest level of an agent-based model and are discrete and abstract computational systems. These automata, which operate within a local and/or universal environment, can be programmed with characteristics of stakeholders and can act independently or interact collectively. Due to this, agent-based models can capture the complexities of a multi-stakeholder environment displaying diversity of behavior and, concurrently, can cater for the changing flood environment. Agent-based models of flood insurance futures have primarily been developed for predictive purposes, such as understanding the impact of introductions of policy instruments. However, the ways in which these situations have been approached by researchers have varied; some have focused on recreating consumer behavior and psychology, while others have sought to recreate agent interactions within a flood environment. The opportunities for agent-based models are likely to become more pronounced as online data becomes more readily available and artificial intelligence technology supports model development.

scholarly journals Second-order agent-based models of emergent behaviour of Dictyostelium discoideum and their inspiration for swarm robotics

2020 ◽  
Vol 25 (4) ◽  
pp. 656-665
Author(s):  
Mohammad Parhizkar ◽  
Giovanna Di Marzo Serugendo ◽  
Jahn Nitschke ◽  
Louis Hellequin ◽  
Assane Wade ◽  
...  
Keyword(s):  
Dictyostelium Discoideum ◽  
Swarm Robotics ◽  
Second Order ◽  
Transition Phase ◽  
Agent Based Models ◽  
Agent Based ◽  
Emergent Behaviour ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Abstract By studying and modelling the behaviour of Dictyostelium discoideum, we aim at deriving mechanisms useful for engineering collective artificial intelligence systems. This paper discusses a selection of agent-based models reproducing second-order behaviour of Dictyostelium discoideum, occurring during the migration phase; their corresponding biological illustrations; and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. For the models, we focus on: (1) the transition phase from first- to second-order emergent behaviour; (2) slugs’ uniform distribution around a light source; and (3) the relationship between slugs’ speed and length occurring during the migration phase of the life cycle of D. discoideum. Results show the impact of the length of the slug on its speed and the effect of ammonia on the distribution of slugs. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on the transition phase, slugs’ chaining, merging and moving away from each other.

scholarly journals Learning differential equation models from stochastic agent-based model simulations

2021 ◽  
Vol 18 (176) ◽  
Author(s):  
John T. Nardini ◽  
Ruth E. Baker ◽  
Matthew J. Simpson ◽  
Kevin B. Flores
Keyword(s):  
Differential Equation ◽  
Cell Biology ◽  
Coarse Grained ◽  
Disease Spread ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Model Simulations ◽  
Differential Equation Models ◽  
Model Dynamics

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth–death–migration model commonly used to explore cell biology experiments and a susceptible–infected–recovered model of infectious disease spread.

scholarly journals Simulation of trends in the use of e-payment using agent based models

2020 ◽  
Vol 1 (1) ◽  
pp. 29
Author(s):  
Elanjati Worldailmi ◽  
Ismianti Ismianti
Keyword(s):  
Economic Policy ◽  
Central Bank ◽  
Modeling And Simulation ◽  
Theoretical Basis ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Economic Activities ◽  
Agent Based ◽  
Online Sales

Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.

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