Livestock Breeding I Ndustries As Complex Adaptive Systems

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Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14

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LIVESTOCK BREEDING INDUSTRIES AS COMPLEX ADAPTIVE SYSTEMS

P.L. Charteris¹, B.L. Golden¹ and D.J. Garrick²

1The Department of Animal Sciences, Colorado State University, Fort Collins CO 80523

2Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand

SUMMARY

Across an entire livestock sector, individual breeders, producers or breeding companies, make breeding decisions. In its broadest sense, nearly any decision made in a livestock-breeding program is equivalent to animal-genetic technological change. The sector-wide impact of animal-genetic technological change has traditionally been modelled without accounting for the physical, economic and social attributes of individual farmers and how these change over time. In contrast, a Complex Adaptive System (CAS) explicitly contains many individuals, which interact with each other to determine their collective welfare. An Agent-Based Model (ABM) is a generic simulation approach to represent a complex adaptive system. The participants in an ABM are termed agents. A livestock sector containing many farmers is an example of a CAS. A properly parameterised ABM would assign attributes and behaviours to agents in a population that can set goals, develop strategies and interact with their environment, including other agents. Agent-based modelling provides a framework for animal-genetic technological change to be simulated as a result of the attributes of and interactions between individual farmers. This approach is also well suited to predicting how emergent structures arise in a livestock sector such as the evolution of nucleus, multiplier and commercial herds over time. This new framework will enhance traditional animal breeding systems research, primarily through being able to evaluate sectoral or industry-level change and how it is influenced by the attributes and interactions of individuals.

Keywords: Complexity, modelling, technology.

INTRODUCTION

Animal geneticists have traditionally employed highly aggregated models to describe the consequences of animal genetic technological change at the sectoral level. The emphasis on tractability allows deductions to be made with relative ease from models that are largely equation- based and mechanistic. An example of this approach is the derivation of economic values from profit equations representative of a single enterprise and their extrapolation to a population (Nitter et al.

1994). Bioeconomic simulation has a higher level of semblance of the modelled system than for single equation-based representations since a greater number of equations can represent complex biological, management and economic parameters (including their interactions), with greater precision. These bioeconomic models can require extensive re-parameterisation when applied to a slightly different system. Neoclassical economic approaches to describing animal genetic technological change (Amer and Fox 1992) have desirable economic properties but usually ignore the decisions made by individual agents and assume change occurs in a timeless environment.

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Some systems cannot be described as the sum of their parts. Rather they have outcomes that emerge over time. Such systems usually contain many diverse inhabitants (such as farmers) that interact with each other and with their environment in intricate ways. The attributes of these inhabitants and the way they respond to their surroundings may change over time in response to past experiences and in anticipation of future events. A new paradigm is required to define these complex systems and a new modelling approach required to describe them. The objective of this paper is to introduce the concepts of Complex Adaptive Systems and Agent-based Modelling within a systems analysis context. Their potential application to animal breeding problems will be discussed.

RESULTS AND DISCUSSION

Complex Adaptive Systems. A system is defined as complex when there are strong interactions among its elements so that current events influence the probability of many kinds of later events.

These complex systems are generally self-organising and have participants that interact in intricate ways to reshape their collective future (Axelrod and Cohen 2000). Examples of complex systems include ecologies, economies, computer networks and cells within an organism. Complexity is the science of complex systems. Complex systems that adapt or evolve to their environment over time are called complex adaptive systems (Holland and Miller 1991). Complex systems typically have emergent structures, something that arises from the interaction of many agents, none of whom necessarily intend this aggregate outcome

Complex system approaches can be used in problem solving and optimisation or in systems analysis.

Within the first field of application, genetic algorithms have been used to optimise breeding program design (Meszaros 1999). This discussion will focus on complex systems approaches applied to systems analysis.

There are a number of CAS modelling approaches including cellular automata (CA), individual-based modelling, agent-based modelling and agent-based computational economics (ACE). For CA, the basic units for modelling are cells on a grid. Such models assign attributes to each cell including the ability to interact with adjacent cells in complex ways. Cellular automata are frequently used for simulating systems with discrete spatial attributes such as trees in a forest or farms in an agricultural community (Balmann 1997). In ecological studies, individual-based models allow individual organisms to interact with one another and with their environment. The individual-based model is extended in ABM by providing agents with strategies and mechanisms that allow them to adapt, learn and evolve. The study of economies modelled as evolving systems of autonomous interacting agents is called agent-based computational economics (Tesfatsion 2001).

Agent-based Modelling. Agent-based models have, as their structure, a population of heterogeneous agents that function together to form a multi-agent system, frequently an economy. Variance among agents allows the population to evolve over time as agents adapt to their environment and learn from their previous experiences. Poorly adapted agents usually but not always exit the system, for example, bull-breeders making little or no genetic gain for traits of economic importance may remain viable through effective marketing. In an economy, agents’ interactions are determined by their own (possibly sub-optimal) decisions, which they make on the basis of locally available incomplete information as well as their own cognitively-limited processing of that information. The outcomes

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from such models are dependent on agent-initiated interactions and occur in a path-dependent timeline. This highlights a key feature of ABM in that they are well suited to systems where behaviour will change over time due to interaction, learning or adaptation (Parrott and Kok 2000).

A drawback of ABM is that obtaining the exact system-level results from previous simulations is impossible when the population comprises multiple agents, each acting in different ways (Balmann 2000; Parrott and Kok 2000). Trends or structures that appear in one iteration of the model may be absent in subsequent iterations. This suggests that models be run over a number of iterations and appropriate analysis applied to discern if macro-level outcomes are significant in a statistical sense.

A further potential pitfall of ABM is that the desire for greater representation of the system results in increasingly more highly parameterised models, with assumptions becoming more ad hoc, perhaps as the researcher moves out of their field of expertise. A sensible stepwise approach to development of an agent-based models would be to develop successive (more complicated) simulations based on successful interpretation of earlier models

Current Agricultural Applications. Multi agent systems have a number of features that make them amenable to the study of agricultural economies (Balmann 2000). Firstly multi-agent systems allow flexible parameter settings; for example, individual agents may have goals and skills that change over time reflecting changes in technology or markets. Secondly self-organisation of industries can emerge. Finally, individual agents may be modelled that have a sense of space, enabling individual farms to be modelled. Only two major applications of ABM in agricultural systems research have been described in the literature. The first is a model established to predict the effect of European pricing policies on structural aspects of individual farms (Balmann 1997). His model framework, based on a CA, showed that twenty-year trends in farm structure and choice of livestock operation were highly dependent on pricing policies. The second study simulated land use change in rural Scotland (Polhill et al. 2001). The aim of their research was to more accurately forecast patterns of land-use based on the attributes of individual farmers and interactions between farmers.

Potential Animal Breeding Applications. Agent-based models can represent a livestock sector or a livestock economy that has many participants and develops emergent structures over time. These emergence approaches explicitly define an environment containing multiple interacting agents (farms) that change over time so would be unsuitable for deriving EV for a single enterprise. This approach may hold promise for deriving EV at a sectoral or industry-level where breeding decisions are decentralised. In such a system, the economic outcome of animal genetic change is a function of decisions made by individual breeders and producers.

The application of agent-based models in animal breeding research will be for testing hypotheses that cannot be answered through other simulation modelling environments. For animal geneticists, two types of hypotheses of interest might be 1) the dynamics of technology uptake and 2) the origin of emergent structures.

Traditional modelling approaches can be used to describe the genetic and economic impact of a new animal genetic technology such as a new EBV but these models are not well suited to predicting how or why this technology may have an impact. The hows and whys requires either field evidence or a

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simulation of behaviour of individual farmers. Agent-based models are well suited to modelling individual decision makers such as farmers. Agents have explicitly defined their spatial and temporal locations in the system as well as algorithms representing learning, communication, imitation, loyalty and risk-taking behaviour. A researcher may hypothesise how learning ability or risk aversion of bull-buyers affects the uptake of a new EBV as well as predicting the genetic and economic impact of this new EBV.

Emergent structures may arise from a combination of physical, economic or social forces. A three- tiered nucleus, multiplier and production sector breeding hierarchy can be described in terms of size and gene flow between sectors. An agent-based approach to modelling a breeding industry may describe how this structure arose in the first place from competition between breeders or loyalty of producers or multipliers to gene-suppliers further up the hierarchy.

A livestock production sector contains a number of farmers, each with different physical, social and economic attributes. On the whole, a livestock sector is a complex evolving system influenced by the attributes of, and actions made by, each of its inhabitants. Agent-based models provide a framework to simulate how the actions and attributes of individual inhabitants affect the global dynamics of the system. This framework will be useful for research organizations to predict the potential genetic or economic impact of new animal-genetic technologies. It is anticipated that this new framework will enhance traditional animal breeding systems research, primarily through being able to evaluate sectoral or industry-level change and how this change is influenced by the attributes and interactions of individuals.

ACKNOWLEDGEMENTS

Funding for Mr. Charteris is provided by the Meat New Zealand Postgraduate Scholarship and the Alan and Grace Kay Overseas Scholarship, Massey University.

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