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.

Uncertainty in Macroeconomic Modelling

2018 ◽  
Author(s):  
Andrew G. Haldane
Keyword(s):  
Economic Systems ◽  
Agent Based Models ◽  
Agent Based ◽  
Macroeconomic Modelling ◽  
Policy Interventions ◽  
Radical Uncertainty ◽  
The Impact

This chapter explores how we might deal with the prevalence of disequilibria and radical uncertainty in complex economic systems, and examines the potential of agent-based models as a tool for helping us understand the dynamics of these systems and the impact of policy interventions.

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