TunaFisher ABM simulates the decisions of fishing companies and fishing vessels of the Philippine tuna purse seinery operating in the Celebes and Sulu Seas.

High fishing effort remains in many of the world’s fisheries, including the Philippine tuna purse seinery, despite a variety of policies that have been implemented to reduce it. These policies have predominantly focused on models of cause and effect which ignore the possibility that the intended outcomes are altered by social behavior of autonomous agents at lower scales.

This model is a spatially explicit Agent-based Model (ABM) for the Philippine tuna purse seine fishery, specifically designed to include social behavior and to study its effects on fishing effort, fish stock and industry profit. The model includes economic and social factors of decision making by companies and fishing vessels that have been informed by interviews.

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The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

This model looks at implications of author/referee interaction for quality and efficiency of peer review. It allows to investigate the importance of various reciprocity motives to ensure cooperation. Peer review is modelled as a process based on knowledge asymmetries and subject to evaluation bias. The model includes various simulation scenarios to test different interaction conditions and author and referee behaviour and various indexes that measure quality and efficiency of evaluation […]

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

The Price Evolution with Expectations model provides the opportunity to explore the question of non-equilibrium market dynamics, and how and under which conditions an economic system converges to the classically defined economic equilibrium. To accomplish this, we bring together two points of view of the economy; the classical perspective of general equilibrium theory and an evolutionary perspective, in which the current development of the economic system determines the possibilities for further evolution.

The Price Evolution with Expectations model consists of a representative firm producing no profit but producing a single good, which we call sugar, and a representative household which provides labour to the firm and purchases sugar.The model explores the evolutionary dynamics whereby the firm does not initially know the household demand but eventually this demand and thus the correct price for sugar given the household’s optimal labour.

The model can be run in one of two ways; the first does not include money and the second uses money such that the firm and/or the household have an endowment that can be spent or saved. In either case, the household has preferences for leisure and consumption and a demand function relating sugar and price, and the firm has a production function and learns the household demand over a set number of time steps using either an endogenous or exogenous learning algorithm. The resulting equilibria, or fixed points of the system, may or may not match the classical economic equilibrium.

In 1985 Dr Michael Palmiter, a high school teacher, first built a very innovative agent-based model called “Simulated Evolution” which he used for teaching the dynamics of evolution. In his model, students can see the visual effects of evolution as it proceeds right in front of their eyes. Using his schema, small linear changes in the agent’s genotype have an exponential effect on the agent’s phenotype. Natural selection therefore happens quickly and effectively. I have used his approach to managing the evolution of competing agents in a variety of models that I have used to study the fundamental dynamics of sustainable economic systems. For example, here is a brief list of some of my models that use “Palmiter Genes”:
- ModEco - Palmiter genes are used to encode negotiation strategies for setting prices;
- PSoup - Palmiter genes are used to control both motion and metabolic evolution;
- TpLab - Palmiter genes are used to study the evolution of belief systems;
- EffLab - Palmiter genes are used to study Jevon’s Paradox, EROI and other things.

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This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management).

The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents.

An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude.

Several taxonomies for empirical validation have been published. Our model integrates different methods to calibrate an innovation diffusion model, ranging from simple randomized input validation to complex calibration with the use of microdata.