Case Studies for a Markov Chain Approach to Analyze Agent-Based Models

Agent-Based Models have become a widely used tool in social sciences, health care management and other disciplines to describe complex systems from a bottom-up perspective. Some reasons for that are the easy understanding of Agent-Based Models, the high flexibility and the possibility to describe heterogeneous structures. Nevertheless problems occur when it comes to analyzing Agent-Based Models. This paper shows how to describe Agent-Based Models in a macroscopic way as Markov Chains, using the random map representation. The focus is on the implementation of this method for chosen examples of a Random Walk and Opinion Dynamic Models. It is also shown how to use Markov Chain tools to analyze these models. Our case studies imply that this method can be a powerful tool when it comes to analyzing Agent-Based Models although some further research in practice is still necessary.

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Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies

Energies ◽

10.3390/en13226133 ◽

2020 ◽

Vol 13 (22) ◽

pp. 6133

Author(s):

Georg Holtz ◽

Christian Schnülle ◽

Malcolm Yadack ◽

Jonas Friege ◽

Thorben Jensen ◽

...

Keyword(s):

Case Studies ◽

Model Development ◽

Stakeholder Involvement ◽

Energy System ◽

Agent Based Models ◽

Agent Based ◽

Industrial Economy ◽

Agent Based Modelling ◽

Model Studies ◽

Phase Out

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies’ impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.

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Filling the Gaps: Vacancy Chains and Agent-Based Models

10.31235/osf.io/nehzj ◽

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.

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