BarterNet is a platform for modeling early barter networks with the aim of learning how supply and demand for a good determine if traders will learn to use that good as a form of money. Traders use a good as money when they offer to trade for it even if they can’t consume it, but believe that they can subsequently trade it for a good they can consume in the near future.

This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

1. The landscape of affordances provided by the material environment,
2. Individual learning and habituation,
3. Social learning and network structure,
4. Personal states (such as habits and attitudes), and
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This is a basic Susceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in a space. In particular, it explores how changing assumptions about the number of susceptible people, starting number of infected people, as well as the disease’s infection probability, and average duration of infection. The model shows that the interactions of agents can drastically affect the results of the model.

We used it in our course on COVID-19: https://www.csats.psu.edu/science-of-covid19

How can a strictly egalitarian social system give way to a stratified society if all of its members punish each other for any type of selfish behavior? This model examines the role of prestige bias in constant and variable environments on the development of hierarchies of wealth.

The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.

This project combines game theory and genetic algorithms in a simulation model for evolutionary learning and strategic behavior. It is often observed in the real world that strategic scenarios change over time, and deciding agents need to adapt to new information and environmental structures. Yet, game theory models often focus on static games, even for dynamic and temporal analyses. This simulation model introduces a heuristic procedure that enables these changes in strategic scenarios with Genetic Algorithms. Using normalized 2x2 strategic-form games as input, computational agents can interact and make decisions using three pre-defined decision rules: Nash Equilibrium, Hurwicz Rule, and Random. The games then are allowed to change over time as a function of the agent’s behavior through crossover and mutation. As a result, strategic behavior can be modeled in several simulated scenarios, and their impacts and outcomes can be analyzed, potentially transforming conflictual situations into harmony.

A simple model is constructed using C# in order to to capture key features of market dynamics, while also producing reasonable results for the individual insurers. A replication of Taylor’s model is also constructed in order to compare results with the new premium setting mechanism. To enable the comparison of the two premium mechanisms, the rest of the model set-up is maintained as in the Taylor model. As in the Taylor example, homogeneous customers represented as a total market exposure which is allocated amongst the insurers.

In each time period, the model undergoes the following steps:
1. Insurers set competitive premiums per exposure unit
2. Losses are generated based on each insurer’s share of the market exposure
3. Accounting results are calculated for each insurer
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We present an agent-based model of worker protest informed by Epstein (2002). Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If […]

EiLab explores the role of entropy in simple economic models. EiLab is one of several models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, and CmLab.

There is a new type of economic model called a capital exchange model, in which the biophysical economy is abstracted away, and the interaction of units of money is studied. Benatti, Drăgulescu and Yakovenko described at least eight capital exchange models – now referred to collectively as the BDY models – which are replicated as models A through H in EiLab. In recent writings, Yakovenko goes on to show that the entropy of these monetarily isolated systems rises to a maximal possible value as the model approaches steady state, and remains there, in analogy of the 2nd law of thermodynamics. EiLab demonstrates this behaviour. However, it must be noted that we are NOT talking about thermodynamic entropy. Heat is not being modeled – only simple exchanges of cash. But the same statistical formulae apply.

In three unpublished papers and a collection of diary notes and conference presentations (all available with this model), the concept of “entropic index” is defined for use in agent-based models (ABMs), with a particular interest in sustainable economics. Models I and J of EiLab are variations of the BDY model especially designed to study the Maximum Entropy Principle (MEP – model I) and the Maximum Entropy Production Principle (MEPP – model J) in ABMs. Both the MEPP and H.T. Odum’s Maximum Power Principle (MPP) have been proposed as organizing principles for complex adaptive systems. The MEPP and the MPP are two sides of the same coin, and an understanding of their implications is key, I believe, to understanding economic sustainability. Both of these proposed (and not widely accepted) principles describe the role of entropy in non-isolated systems in which complexity is generated and flourishes, such as ecosystems, and economies.

EiLab is one of several models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, and CmLab.