Computational Model Library

This model simulations social and childcare provision in the UK. Agents within simulated households can decide to provide for informal care, or pay for private care, for their loved ones after they have provided for childcare needs. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. This model extends our previous simulations of social care by simulating the impact of childcare demand on social care availability within households, which is known to be a significant constraint on informal care provision.

Results show that our model replicates realistic patterns of social and child care provision, suggesting that this framework can be a valuable aid to policy-making in this area.

The purpose of this model is the simulation of social care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need and geographical location. The model simulates care provision as a negotiation process conducted between agents across their kinship networks, with agents with stronger familial relationships to the recipient being more likely to attempt to allocate time to care provision. The model also simulates demographic change, the impact of socioeconomic status, and allows agents to relocate and change jobs or reduce working hours in order to provide care.
Despite the relative lack of empirical data in this model, the model is able to reproduce plausible patterns of social care provision. The inclusion of detailed economic and behavioural mechanisms allows this model to serve as a useful policy development tool; complex behavioural interventions can be implemented in simulation and tested on a virtual population before applying them in real-world contexts.

The model measures drivers of effectiveness of risk assessments in risk workshops regarding the correctness and required time. Specifically, we model the limits to information transfer, incomplete discussions, group characteristics, and interaction patterns and investigate their effect on risk assessment in risk workshops.

The model simulates a discussion in the context of a risk workshop with 9 participants. The participants use Bayesian networks to assess a given risk individually and as a group.

This is model that explores how a few farmers in a Chinese village, where all farmers are smallholders originally, reach optimal farming scale by transferring in farmland from other farmers in the context of urbanization and aging.


Meike Will Jürgen Groeneveld Karin Frank Birgit Müller Friederike Lenel | Published Mon Jul 20 13:41:17 2020 | Last modified Mon May 3 16:26:34 2021

The fight against poverty is an urgent global challenge. Microinsurance is promoted as a valuable instrument for buffering income losses due to health or climate-related risks of low-income households in developing countries. However, apart from direct positive effects they can have unintended side effects when insured households lower their contribution to traditional arrangements where risk is shared through private monetary support.

RiskNetABM is an agent-based model that captures dynamics between income losses, insurance payments and informal risk-sharing. The model explicitly includes decisions about informal transfers. It can be used to assess the impact of insurance products and informal risk-sharing arrangements on the resilience of smallholders. Specifically, it allows to analyze whether and how economic needs (i.e. level of living costs) and characteristics of extreme events (i.e. frequency, intensity and type of shock) influence the ability of insurance and informal risk-sharing to buffer income shocks. Two types of behavior with regard to private monetary transfers are explicitly distinguished: (1) all households provide transfers whenever they can afford it and (2) insured households do not show solidarity with their uninsured peers.

The model is stylized and is not used to analyze a particular case study, but represents conditions from several regions with different risk contexts where informal risk-sharing networks between smallholder farmers are prevalent.

A generalized organizational agent- based model (ABM) containing both formal organizational hierarchy and informal social networks simulates organizational processes that occur over both formal network ties and informal networks.

TERRoir level Organic matter Interactions and Recycling model

Myriam Grillot | Published Wed Apr 19 14:33:44 2017 | Last modified Wed Jun 17 14:13:35 2020

The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.

Informal City version 1.0

Nina Schwarz | Published Fri Jul 25 15:11:26 2014 | Last modified Thu Jul 30 07:35:00 2015

InformalCity, a spatially explicit agent-based model, simulates an artificial city and allows for testing configurations of urban upgrading schemes in informal settlements.

Amazon smallholder resilience

Yue Dou | Published Mon Sep 9 19:35:39 2019

The purpose of this agent-based model is to simulate the behaviors of small farming households in the Amazon estuary region and evaluate their resilience to external shocks with the presence of several government cash transfer programs.

PolicySpace models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. The model contains citizens, markets, residences, municipalities, commuting and a the tax scheme. In the associated publications (book in press and we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities’ quality of life.

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