Above are papers related to the following paper: [Paper_ID] Paper #1117049. 	[Title] Models in Evolutionary Ecology and the Validation Problem Caswell for their guidance and support. evolutionary [Abstract] All models of natural systems represent an abstraction and simplification of that system. Thus all models suffer a validation problem. Should we believe that the results of the model have any bearing on reality? This is a particularly acute problem for Alife models of evolution and ecosystems. The time scale of evolution and the complexity of ecosystems make controlled experiments difficult. If Alife is ever to contribute significantly to biology, we must find methods by which we can build confidence in our models. One alternative to experimental tests of a model is to validate it against previously verified theory. I have applied a series of ecological and evolutionary validation tests to a model of species diversification. Examination of the predator-prey dynamics, trophic cascades, competitive exclusion, adaptation, and the species-area curve in the model has shown that a course grained spatial structure was inadequate to capture the realistic dynamics of an ecosystem. Only when spatial structure was extended to the local patch dynamics did the model begin to behave realistically under a wide range of parameters. Validation of the ecological dynamics of the model provides indirect support for the evolutionary behavior of the species within the ecosystem. Every model is an abstraction and a simplification. The goal of a model is to capture the essence of a system in the real world such that the behavior of the model matches the behavior of the real system. Thus for any model we may ask if it is a valid representation of the real system. Answering this question is the problem of validation. Traditionally we can try to disprove the validity of the model by collecting data from the real system and comparing it to the predictions of the model. In artificial life we rarely have that luxury. Artificial life models tend to be highly abstract and general because the field is striving to discover general properties of life. This makes experimental validation extremely difficult. The time scale of evolution tends to restrict experiments to observation of the fossil record (Benton 1990, for example) or manipulation of organisms with extremely short life-cycles in simplified environments (Krukonis 1996, for example). Similarly, the complexity and size of ecosystems makes ecological experiments cumbersome and difficult to control. An alternative form of validation can be pursued indirectly through reference to ecological and evolutionary theory. Instead of asking if the model matches the experimental data, we can ask if the model matches our understanding of the dynamics of ecology and evolution. Then, to the extent that the theories of ecology and evolution have been validated by experimental observations, we can disprove the validity of a model when it fails to match those theories. What follows is an example of this technique applied to a model designed to examine the factors that impact the origin and maintenance of species diversity. While the purpose of this model is to explore new theoretical ground in biology, the ecological and evolutionary dynamics in the model have been validated against theories of predation, competition, adaptation and island biogeography. Hraber and Milne (1997) looked at genotype diversity under the presence or absence of selection and varying mutation rates in the ECHO model (Holland 1992; 1993). Mirroring Bedau et al.'s (1992) results, they found that genotypic diversity was greatest under Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #321004. 	[Title] The Ecology of Echo Echo is a generic ecosystem model in which evolving agents are [Abstract]  Please summarise all the given information.

Paper #1117049 has the title `Models in Evolutionary Ecology and the Validation Problem Caswell for their guidance and support. evolutionary
` and the abstract `All models of natural systems represent an abstraction and simplification of that system. Thus all models suffer a validation problem. Should we believe that the results of the model have any bearing on reality? This is a particularly acute problem for Alife models of evolution and ecosystems. The time scale of evolution and the complexity of ecosystems make controlled experiments difficult. If Alife is ever to contribute significantly to biology, we must find methods by which we can build confidence in our models. One alternative to experimental tests of a model is to validate it against previously verified theory. I have applied a series of ecological and evolutionary validation tests to a model of species diversification. Examination of the predator-prey dynamics, trophic cascades, competitive exclusion, adaptation, and the species-area curve in the model has shown that a course grained spatial structure was inadequate to capture the realistic dynamics of an ecosystem. Only when spatial structure was extended to the local patch dynamics did the model begin to behave realistically under a wide range of parameters. Validation of the ecological dynamics of the model provides indirect support for the evolutionary behavior of the species within the ecosystem. Every model is an abstraction and a simplification. The goal of a model is to capture the essence of a system in the real world such that the behavior of the model matches the behavior of the real system. Thus for any model we may ask if it is a valid representation of the real system. Answering this question is the problem of validation. Traditionally we can try to disprove the validity of the model by collecting data from the real system and comparing it to the predictions of the model. In artificial life we rarely have that luxury. Artificial life models tend to be highly abstract and general because the field is striving to discover general properties of life. This makes experimental validation extremely difficult. The time scale of evolution tends to restrict experiments to observation of the fossil record (Benton 1990, for example) or manipulation of organisms with extremely short life-cycles in simplified environments (Krukonis 1996, for example). Similarly, the complexity and size of ecosystems makes ecological experiments cumbersome and difficult to control. An alternative form of validation can be pursued indirectly through reference to ecological and evolutionary theory. Instead of asking if the model matches the experimental data, we can ask if the model matches our understanding of the dynamics of ecology and evolution. Then, to the extent that the theories of ecology and evolution have been validated by experimental observations, we can disprove the validity of a model when it fails to match those theories. What follows is an example of this technique applied to a model designed to examine the factors that impact the origin and maintenance of species diversity. While the purpose of this model is to explore new theoretical ground in biology, the ecological and evolutionary dynamics in the model have been validated against theories of predation, competition, adaptation and island biogeography. Hraber and Milne (1997) looked at genotype diversity under the presence or absence of selection and varying mutation rates in the ECHO model (Holland 1992; 1993). Mirroring Bedau et al.'s (1992) results, they found that genotypic diversity was greatest under 
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Question: Which category should Paper #1117049 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1117049 has the title `Models in Evolutionary Ecology and the Validation Problem Caswell for their guidance and support. evolutionary
` and the abstract `All models of natural systems represent an abstraction and simplification of that system. Thus all models suffer a validation problem. Should we believe that the results of the model have any bearing on reality? This is a particularly acute problem for Alife models of evolution and ecosystems. The time scale of evolution and the complexity of ecosystems make controlled experiments difficult. If Alife is ever to contribute significantly to biology, we must find methods by which we can build confidence in our models. One alternative to experimental tests of a model is to validate it against previously verified theory. I have applied a series of ecological and evolutionary validation tests to a model of species diversification. Examination of the predator-prey dynamics, trophic cascades, competitive exclusion, adaptation, and the species-area curve in the model has shown that a course grained spatial structure was inadequate to capture the realistic dynamics of an ecosystem. Only when spatial structure was extended to the local patch dynamics did the model begin to behave realistically under a wide range of parameters. Validation of the ecological dynamics of the model provides indirect support for the evolutionary behavior of the species within the ecosystem. Every model is an abstraction and a simplification. The goal of a model is to capture the essence of a system in the real world such that the behavior of the model matches the behavior of the real system. Thus for any model we may ask if it is a valid representation of the real system. Answering this question is the problem of validation. Traditionally we can try to disprove the validity of the model by collecting data from the real system and comparing it to the predictions of the model. In artificial life we rarely have that luxury. Artificial life models tend to be highly abstract and general because the field is striving to discover general properties of life. This makes experimental validation extremely difficult. The time scale of evolution tends to restrict experiments to observation of the fossil record (Benton 1990, for example) or manipulation of organisms with extremely short life-cycles in simplified environments (Krukonis 1996, for example). Similarly, the complexity and size of ecosystems makes ecological experiments cumbersome and difficult to control. An alternative form of validation can be pursued indirectly through reference to ecological and evolutionary theory. Instead of asking if the model matches the experimental data, we can ask if the model matches our understanding of the dynamics of ecology and evolution. Then, to the extent that the theories of ecology and evolution have been validated by experimental observations, we can disprove the validity of a model when it fails to match those theories. What follows is an example of this technique applied to a model designed to examine the factors that impact the origin and maintenance of species diversity. While the purpose of this model is to explore new theoretical ground in biology, the ecological and evolutionary dynamics in the model have been validated against theories of predation, competition, adaptation and island biogeography. Hraber and Milne (1997) looked at genotype diversity under the presence or absence of selection and varying mutation rates in the ECHO model (Holland 1992; 1993). Mirroring Bedau et al.'s (1992) results, they found that genotypic diversity was greatest under 
`.
Question: Which category should Paper #1117049 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms