Statistical analysis of spatially homogeneous dynamic agent-based processes using functional time series analysis
Dynamic systems consisting of multiple interacting autonomous individuals are of particular interest in a number of scientific fields, including ecology, biology, and swarm robotics. Such systems are commonly referred to as agent-based processes. Detection and characterisation of agent–agent interac...
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| Published in | Spatial statistics Vol. 17; pp. 199 - 219 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.08.2016
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| Subjects | |
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| Abstract | Dynamic systems consisting of multiple interacting autonomous individuals are of particular interest in a number of scientific fields, including ecology, biology, and swarm robotics. Such systems are commonly referred to as agent-based processes. Detection and characterisation of agent–agent interactions is an important step in the analysis of agent-based processes, however existing statistical methods are relatively limited. This paper presents a novel framework for investigating spatial interactions between agents combining techniques from spatial statistics and functional time series analysis. Assuming second order spatial equilibrium of the agent-based process, we develop a test for identifying the specific nature of interactions between agents. We also consider methodology for validating the assumption of spatial equilibrium for a given realisation of the agent-based process. The efficacy of this methodology is demonstrated via Monte Carlo simulation studies and an application to experimental data obtained by observing a species of flightless locust. |
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| AbstractList | Dynamic systems consisting of multiple interacting autonomous individuals are of particular interest in a number of scientific fields, including ecology, biology, and swarm robotics. Such systems are commonly referred to as agent-based processes. Detection and characterisation of agent–agent interactions is an important step in the analysis of agent-based processes, however existing statistical methods are relatively limited. This paper presents a novel framework for investigating spatial interactions between agents combining techniques from spatial statistics and functional time series analysis. Assuming second order spatial equilibrium of the agent-based process, we develop a test for identifying the specific nature of interactions between agents. We also consider methodology for validating the assumption of spatial equilibrium for a given realisation of the agent-based process. The efficacy of this methodology is demonstrated via Monte Carlo simulation studies and an application to experimental data obtained by observing a species of flightless locust. |
| Author | Read, Mark N. Rice, Gregory Hywood, Jack D. |
| Author_xml | – sequence: 1 givenname: Jack D. surname: Hywood fullname: Hywood, Jack D. email: jhyw1620@uni.sydney.edu.au organization: Sydney Medical School, The University of Sydney, Sydney, Australia – sequence: 2 givenname: Mark N. surname: Read fullname: Read, Mark N. organization: School of Life and Environmental Sciences & Charles Perkins Centre, University of Sydney, Sydney, Australia – sequence: 3 givenname: Gregory surname: Rice fullname: Rice, Gregory organization: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada |
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| CitedBy_id | crossref_primary_10_1098_rsif_2020_0879 crossref_primary_10_1111_jtsa_12447 crossref_primary_10_1016_j_jclepro_2020_123091 crossref_primary_10_1371_journal_pcbi_1011361 |
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| Keywords | Functional time series Agent-based models Spatio-temporal point process Agent-based processes Moment dynamics Spatial statistics |
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