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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 first simple movement models used unbiased and uncorrelated random walks (RW). In such models of movement, the direction of the movement is totally independent of the previous movement direction. In other words, at each time step the direction, in which an individual is moving is completely random. This process is referred to as a Brownian motion.
On the other hand, in correlated random walks (CRW) the choice of the movement directions depends on the direction of the previous movement. At each time step, the movement direction has a tendency to point in the same direction as the previous one. This movement model fits well observational movement data for many animal species.
The presented agent based model simulated the movement of the agents as a correlated random walk (CRW). The turning angle at each time step follows the Von Mises distribution with a ϰ of 10. The closer ϰ gets to zero, the closer the Von Mises distribution becomes uniform. The larger ϰ gets, the more the Von Mises distribution approaches a normal distribution concentrated around the mean (0°).
This model is implemented in python and can be used as a building block for more complex agent based models that would rely on describing the movement of individuals with CRW.
Artificial Anasazi is an agent-based model of farming households in the Long House Valley, Arizona, from 800 to 1350 CE. Each household chooses where to farm and settle based on expected harvest, water sources and stored maize, and simulated household counts are compared with the archaeological record.
This release adapts the NetLogo Models Library version (Stonedahl & Wilensky 2010) of Janssen’s (2009) NetLogo replication of the original Ascape model (Dean et al. 2000; Axtell et al. 2002). It contains four configurations in one NetLogo 7.0.4 file, selected with two switches:
soil-on?: dynamic soil quality that depletes under cultivation and regrows, following Janssen (2010, eq. 4). Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.
A “Ger” is a yurt style house used by pastoralists in Mongolia. This model simulates seasonal movements, fission/fusion dynamics, social interaction between households and how these relate to climate impacts.
Using data from the British Social Attitude Survey, we develop an agent-based model to study the effect of social influence on the spread of meat-eating behaviour in the British population.
This model describes a mechanism by which software agents can identify norms in an artificial agent society. In particular, this model uses a sequence mining approach to identify norms in an agent soc
This is a short NetLogo example demonstrating how to initialize 500 agents with 4 correlated parameters each with random values by doing the necessary calculations in the program “R” and retrieving the results.
The purpose of the model is to provide an analogy for how the Schwartz values may influence the aggregated economic performance, as measured by: public goods provision, private goods provision and leisure time.
This repository contains the replication materials for the JASSS submission: ‘Indirect Reciprocity with Contagious Reputation in Large-Scale Small-World Networks’. Further detail on how to run the models is provided in README.txt.
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