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Reconstruction of the original code of the classical model M. Cohen, J. March, and J. Olsen (garbage can model, GCM or CMO). Using the obtained tool detected that the content model of many researchers interpreted the surface, due to the lack of attention to the algorithmic part of the source. Found the unstable behavior of the system under heavy load and unsegmented access and decision structures, set Load variable value ranges influencing the behavior of the system. Also (in the author’s article) carried out a logical analysis of the system of arbitrary dimension and the analytical result obtained for the case of unsegmented access and decision structures under light, moderate and heavy load. Reconstruction realized by means of Microsoft Office Excel 2010. The code is implemented as a Visual Basic macro, input and output data (log with numerically designated garbage cans) are placed on Excel sheets. The most complex pieces of code commented, for such fragments indicated the corresponding Visual Basic lines to the original model FORTRAN code lines.
Data Types:
  • Software/Code
The concept of GCM is decision making, which resolve Problems. Problems have Energy Required (ER) for their decision, Managers (decision makers) have Effective Energy (EE) for the decisions generation. The place of decision-making is the Choice Opportunities (Choices) - abstraction of commitees, consul boards and so on. The Choices and Problems open randomly. The Problems activated by two items and Choices by one item per step of modellig (tick) in first halfe of modelling time. All Managers are ready for action from first modelling step. Problems and Managers access to Choices in according with system structrure. There is three types of Problems access to Choices and three types of Managers access to Choices. Presented model literally reproduces the original logic of GCM (with one exception: the equal effective energy distribution between managers assumed).
Data Types:
  • Software/Code
In a two-level hierarchical structure (consisting of the positions of managers and operators), persons holding these positions have a certain performance and the value of their own (personal perception in this, simplified, version of the model) perception of each other. The value of the perception of each other by agents is defined as a random variable that has a normal distribution (distribution parameters are set by the control elements of the interface). In the world of the model, which is the space of perceptions, agents implement two strategies: rapprochement with agents that perceive positively and distance from agents that perceive negatively (both can be implemented, one of these strategies, or neither, the other strategy, which makes the agent stationary). Strategies are implemented in relation to those agents that are in the radius of perception (PerRadius). The manager (Head) forms a team of agents. The performance of the group (the sum of the individual productivities of subordinates, weighted by the distance from the leader) varies depending on the position of the agents in space and the values of their individual productivities. Individual productivities, in the current version of the model, are set as a random variable distributed evenly on a numerical segment from 0 to 100. The manager forms the team 1) from agents that are in (organizational) radius (Op_Radius), 2) among agents that the manager perceives positively and / or negatively (both can be implemented, one of the specified rules, or neither, which means the refusal of the command formation). Agents can (with a certain probability, given by the variable PrbltyOfDecisn%), in case of a negative perception of the manager, leave his group permanently. It is possible in the model to change on the fly radii values, update the perception value across the entire population and the perception of an individual agent by its neighbors within the perception radius, and the probability values for a subordinate to make a decision about leaving the group. You can also change the set of strategies for moving agents and strategies for recruiting a team manager. It is possible to add a randomness factor to the movement of agents (Stoch_Motion_Speed, the default is set to 0, that is, there are no random movements).
Data Types:
  • Software/Code
The Palaeo-Agulhas Plain formed an important habitat exploited by Pleistocene hunter-gatherer populations during periods of lower sea level. This productive, grassy habitat would have supported numerous large-bodied ungulates accessible to a population of skilled hunters with the right hunting technology. It also provided a potentially rich location for plant food collection, and along its shores a coastline that moved with the rise and fall of sea levels. The rich archaeological and paleontological records of Pleistocene sites along the modern Cape south coast of South Africa, which would have overlooked the Palaeo-Agulhas Plain during Pleistocene times of lower sea level, provides a paleoarchive of this extinct ecosystem. In this paper, we present a first order illustration of the “palaeoscape modeling” approach advocated by Marean et al. (2015). We use a resourcescape model created from modern studies of habitat productivity without the Palaeo-Agulhas Plain. This is equivalent to predominant Holocene conditions. We then run an agent-based model of the human foraging system to investigate several research questions. Our agent-based approach uses the theoretical framework of optimal foraging theory to model human foraging decisions designed to optimize the net caloric gains within a complex landscape of spatially and temporally variable resources. We find that during the high sea-levels of MIS 5e (+5-6 m asl) and the Holocene, the absence of the Plain left a relatively poor food base supporting a much smaller population relying heavily on edible plant resources from the current Cape flora. Despite high species diversity of plants with edible storage organs, and marine invertebrates, encounter rates with highly profitable resources were low. We demonstrate that without the Palaeo-Agulhas Plain, human populations must have been small and low density, and exploited plant, mammal, and marine resources with relatively low caloric returns. The exposure and contraction of the Palaeo-Agulhas Plain was likely the single biggest driver of behavioral change during periods of climate change through the Pleistocene and into the transition to the Holocene.
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  • Software/Code
The CHIME ABM was designed to investigate the dynamics of hazardous weather communication and decision making, in the context of evolving hazard forecasts and the complex modern information environment. To ensure that the model included important elements, dynamics, and interactions needed to address the questions of interest, it was conceptualized and implemented through interdisciplinary collaboration incorporating in-depth knowledge about key aspects of the system. Areas of expertise involved include hazardous weather and weather forecasting as well as hazard risk communication, information networks, vulnerability, and protective decision making. This knowledge was incorporated through interactions between the ABM research and the larger research project discussed in Morss et al. (BAMS 2017) and review of relevant literature. CHIME ABM V1 includes a modeled world consisting of a spatially explicit geographical area of interest (e.g., the US state of Florida), a dynamic hazard that moves through that world (a hurricane), evolving forecast information about that hazard, and a multi-agent model in which different types of information related to the hazard are communicated among agents and used in protective decision making. The version of the model described here has been developed and tested in the NetLogo modeling environment. Most of the past and current code development was done in NetLogo 5x, relying heavily on the GIS extension and maps or spatial data imported from several sources (described in the supporting documentation). V1.4 is compatible with NetLogo 6x, includes a new geographic region, and includes options for recent major hurricanes.
Data Types:
  • Software/Code
The command and control policy in natural resource management, including water resources, is a longstanding established policy that has been theoretically and practically argued from the point of view of social-ecological complex systems. With the intention of making a system ecologically resilient, these days, policymakers apply the top-down policies of controlling communities through regulations. To explore how these policies may work and to understand whether the ecological goal can be achieved via command and control policy, this research uses the capacity of Agent-Based Modeling (ABM) as an experimental platform in the Urmia Lake Basin (ULB) in Iran, which is a social-ecological complex system and has gone through a drought process. Despite the uncertainty of the restorability capacity of the lake, there has been a consensus on the possibility to artificially restore the lake through the nationally managed Urmia Lake Restoratoin Program (ULRP). To reduce water consumption in the Basin, the ULRP widely targets the agricultural sector and proposes the project of changing crop patterns from high-water-demand (HWD) to low-water-demand (LWD), which includes a component to control water consumption by establishing water-police forces. Using a wide range of multidisciplinary studies about Urmia Lake at the Basin and sub-basins as well as qualitative information at micro-level as the main conceptual sources for the ABM, the findings under different strategies indicate that targeting crop patterns change by legally limiting farmers’ access to water could force farmers to change their crop patterns for a short period of time as long as the number of police constantly increases. However, it is not a sustainable policy for either changing the crop patterns nor restoring the lake.
Data Types:
  • Software/Code
The model that simulates the dynamic creation and maintenance of knowledge-based formations such as communities of scientists, fashion movements, and subcultures. The model’s environment is a spatial one, representing not geographical space, but a “knowledge space” in which each point is a different collection of knowledge elements. Agents moving through this space represent people’s differing and changing knowledge and beliefs. The agents have only very simple behaviors: If they are “lonely,” that is, far from a local concentration of agents, they move toward the crowd; if they are crowded, they move away. Running the model shows that the initial uniform random distribution of agents separates into “clumps,” in which some agents are central and others are distributed around them. The central agents are crowded, and so move. In doing so, they shift the centroid of the clump slightly and may make other agents either crowded or lonely, and they too will move. Thus, the clump of agents, although remaining together for long durations (as measured in time steps), drifts across the view. Lonely agents move toward the clump, sometimes joining it and sometimes continuing to trail behind it. The clumps never merge. The model is written in NetLogo (v6). It is used as a demonstration of agent-based modelling in Gilbert, N. (2008) Agent-Based Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc. and described in detail in Gilbert, N. (2007) “A generic model of collectivities,” Cybernetics and Systems. European Meeting on Cybernetic Science and Systems Research, 38(7), pp. 695–706.
Data Types:
  • Software/Code
This model tries to mimic human behavior in a topographical environment. It aims to go beyond the GIS approach to least-cost path that requires perfect knowledge of the whole environment to choose the best path between two points. This model is different in that the agent does not have a perfect knowledge of the whole surface, but rather evaluates the best path locally, at each step, thus mimicking imperfect human behavior more accurately. It relies on the work by Naismith (1892, in Aitken 1977) and Langmuir (1984) on walking time expenditure in rugged environments. Their walking time values are used to calculate the agent’s travel time.
Data Types:
  • Software/Code
Data Types:
  • Software/Code
An ABM simulating white-tailed deer population dynamics for selected Michigan counties. The model yields pre-harvest and post-harvest realistic population snapshots that can be used to initialize the surveillance model (MIOvPOPsurveillance) and the CWD transmission dynamics model (MIOvCWD) respectively.
Data Types:
  • Software/Code