Agent-based rumor spreading models for human geography applications

Zare, Alina; Fields, Zachary; Keller, James M.; Horton, J. F.

doi:10.1109/igarss.2012.6352387

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Using Agent-Based Modeling (ABM), we studied disaster evacuation scenarios [1][2][3] within a human geographic framework. This included decision making via bounded rationality and several communication schemes implemented as rumor spreading models.…”

Section: Introductionmentioning

confidence: 99%

A human geospatial predictive analytics framework with application to finding medically underserved areas

Keller

Buck

Zare

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD)

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Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. In this paper, we study the utility of a human geographic data cube coupled with computational intelligence as a means to predict conditions across a geographic area. This becomes a Big data problem. In this sense, we are using genotype information to predict phenotype states. We demonstrate the approach on the prediction of medically underserved areas in Missouri.

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Section: Introductionmentioning

confidence: 99%

A human geospatial predictive analytics framework with application to finding medically underserved areas

Keller

Buck

Zare

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD)

Self Cite

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“…By using agents to represent individual decisionmakers, complex systems can be created that show emergent behavior (Holland and Miller 1991). In the context of human geography, agent-based models have been used to study evacuation scenarios in disaster situations (Keller, Popescu, and Gibeson 2012), and utilized concepts such as bounded rationality to guide agent behavior (Popescu and Keller 2012) and rumor spreading models based on social networks (Zare et al 2012). A related field is that of multi-agent systems, which focuses on using an interacting group of intelligent agents to model problems that are too complex for an individual agent (Niazi and Hussain 2011).…”

Section: The Traveling Salesman Problemmentioning

confidence: 99%

Multicriteria pathfinding in uncertain simulated environments

Buck¹

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Multicriteria decision-making problems arise in all aspects of daily life and form the basis upon which high-level models of thought and behavior are built. These problems present various alternatives to a decision-maker, who must evaluate the trade-offs between each one and choose a course of action. In a sequential decision-making problem, each choice can influence which alternatives are available for subsequent actions, requiring the decision-maker to plan ahead in order to satisfy a set of objectives. These problems become more difficult, but more realistic, when information is restricted, either through partial observability or by approximate representations. Pathfinding in partially observable environments is one significant context in which a decision-making agent must develop a plan of action that satisfies multiple criteria. In general, the partially observable multiobjective pathfinding problem requires an agent to navigate to certain goal locations in an environment with various attributes that may be partially hidden, while minimizing a set of objective functions. To solve these types of problems, we create agent models based on the concept of a mental map that represents the agent's most recent spatial knowledge of the environment, using fuzzy numbers to represent uncertainty. We develop a simulation framework that facilitates the creation and deployment of a wide variety of environment types, problem definitions, and agent models. This computational mental map (CMM) framework is shown to be suitable for studying various types of sequential multicriteria decision-making problems, such as the shortest path problem, the traveling salesman problem, and the traveling purchaser problem in multiobjective and partially observable configurations.

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