Speaker: Dr. Karun Kaniyamattam, Assistant Professor of Livestock Data Analytics and Artificial Intelligence Modeling, Department of Animal Science, Texas A&M University

Faculty Hosts: Jian Tao, PVFA/TAMIDS

Abstract: The earliest known history of agent-based modeling (ABM) is traced back to Von Neumann’s self-reproducing cellular automata, designed in the 1940s. The usage of ABM in different scientific fields accelerated in the 1990s, supported by the phenomenal increase in computing capabilities. ABM is gaining acceptance in livestock systems, as daily decision-making is becoming complex due to multiple competing outcomes of interest to food systems stakeholders. ABM typically replicates complex real-world systems, for example, a herd of animals that dynamically interact based on simple rules. ABM simulates the heterogeneous, stochastic characteristics of agents observed in the real-world. The dynamic interaction of agents replicates the observable real-world complex system patterns. Application of ABM in animal systems ranges from modeling cell behavior, precision nutrition, and ​ herd management to predicting the spread of epidemics and food animal supply chain optimization. Animal science’s most widely used alternative modeling techniques include system dynamic models, differential equations-based models, and statistical modeling. The list of unrealistic assumptions that limit the utility of these modeling techniques includes the assumptions of linearity, homogeneity, normality, and stationarity. The advantageous characteristics of an agent in ABM that set it apart from other techniques include being identifiable, capable of existing in an environment where it interacts with other agents while being autonomous and self-directed, goal-oriented behavior, flexible learning, and the capability of adaptations in its behavior over time, based on experience. Identifying the purpose of the model, the questions the model will answer, and the potential users are the key decision variables modelers should ponder before embarking on building ABM. The commonly used ABM software includes Repast, Swarm, Netlogo, and MASON. Developing an ABM has several highly interleaved stages: concept development, requirements definition, design, implementation, and operationalization, each of which will be illustrated during this exciting session on ABM.

Biography: Dr. Karun Kaniyamattam is an Assistant Professor of Livestock Data Analytics and Artificial Intelligence Modeling at the Texas A&M Department of Animal Science, internationally recognized for advancing AI solutions for sustainable livestock systems. His work focuses on livestock disease modeling, supply chain economics, climate-smart cattle production—including optimized nutrition and greenhouse gas footprint mitigation—and the application of smart technologies and artificial intelligence to enhance livestock resilience. A trained veterinary epidemiologist (Cornell) and livestock systems modeler (UF), Karun has produced more than 40 peer-reviewed publications, 23 different decision models, and secured over $3 million in research funding. Karun has directly influenced epidemiology and animal agriculture policy and practice globally, contributing to smart cattle management practices in South Africa, Tropical cattle production in India under climatic and epidemiological constraints, and AI workforce development initiatives globally. He leads programing sub-committee of the Feed Management Committee of the National Animal Nutrition Program and serves on the Emerging and Contemporary Issues Committee of the American Society of Animal Science. A dedicated mentor and leader, Karun has guided ~250 students, numerous faculty, extension professionals, and international scholars through his Artificial Intelligence for Sustainable Food Systems Initiative at Texas A&M University.