Can agent-based models assist decisions on large-scale practical problems? A philosophical analysis

Gross, D.; Strand, Roger

doi:10.1002/1099-0526(200007/08)5:6<26::aid-cplx6>3.0.co;2-g

Cited by 39 publications

(11 citation statements)

References 17 publications

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“…Retrodiction compares the states of the model with past observations of the target system. Lastly, structural similarity refers to assessing the realism of the structure of the model in terms of empirical and/or theoretical knowledge of the target system (see also Gross and Strand 2000). In practice, all three approaches are interdependent and no single approach is used alone.…”

Section: Broad Types Of Validitymentioning

confidence: 99%

“…Validation through prediction requires matching the model with aspects of the target system before they were observed. The logic of predictive validity is the following: if one is using a predictive model-in which the purpose of the model is to predict future states of the target system-and the predictions prove satisfactory in repeated tested events, it may be reasonable to expect the model outcomes to stay reliable under similar conditions (Gross and Strand 2000). The purpose of prediction is somewhat problematic in social simulation:…”

Section: Validity Through Predictionmentioning

confidence: 99%

“…Most involve human beings and are too valuable to allow repeated intervention, which hinders the acquisition of knowledge about its future behaviour. Policies based on false predictions could have serious consequences, thus making the purpose of prediction unusable (Gross and Strand 2000).…”

Section: Validity Through Predictionmentioning

confidence: 99%

“…The difference from retrodiction to prediction is that in the former the intention is to reproduce already observed aspects of the target system. Given the existence of a historical record of facts from the target system, the rationale of retrodictive validity for a predictive model is the following: If the model is able to reproduce a historical record consistently and correctly, then the model may also be trusted for the future (Gross and Strand 2000). However, as we have mentioned, predictive models of social complexity are uncommon in simulation.…”

Section: Validity Through Retrodictionmentioning

confidence: 99%

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Verifying and Validating Simulations

David

Fachada²,

Rosa³

2017

Understanding Complex Systems

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Verification and validation are two important aspects of model building. Verification and validation compare models with observations and descriptions of the problem modelled, which may include other models that have been verified and validated to some level. However, the use of simulation for modelling social complexity is very diverse. Often, verification and validation do not refer to an explicit stage in the simulation development process, but to the modelling process itself, according to good practices and in a way that grants credibility to using the simulation for a specific purpose. One cannot consider verification and validation without considering the purpose of the simulation. This chapter deals with a comprehensive outline of methodological perspectives and practical uses of verification and validation. The problem of evaluating simulations is addressed in four main topics: (1) the meaning of the terms verification and validation in the context of simulating social complexity; (2) types of validation, as well as techniques for validating simulations; (3) model replication and comparison as cornerstones of verification and validation; and (4) the relationship of various validation types and techniques with different modelling strategies. Why Read This Chapter? To help you decide how to check your simulation-both against its antecedent conceptual models (verification) and external standards such as data or other simulations (validation)-and in this way help you to establish the credibility of your simulation. In order to do this the chapter will point out the nature of these processes, including the variety of ways in which people seek to achieve them.

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Section: Broad Types Of Validitymentioning

confidence: 99%

Section: Validity Through Predictionmentioning

confidence: 99%

Section: Validity Through Predictionmentioning

confidence: 99%

Section: Validity Through Retrodictionmentioning

confidence: 99%

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Verifying and Validating Simulations

David

Fachada²,

Rosa³

2017

Understanding Complex Systems

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“…They have been used to model plants for animated movies3 and to analyze and model other complex data, such as music4. Hornby5 uses graphs to model the modularity and reuse of concepts in engineering design and Gross and Strand6 use L‐systems for decision making on large‐scale practical problems. Kunihiko Kaneko refers to L‐systems when he discusses the relation between rules and patterns, in particular, the complementarities between syntax/rule/parts and semantics/behavior/whole in living systems7.…”

Section: This Matlab™ Code Is Used To Create the Graphs In Figurementioning

confidence: 99%

Probabilistic L‐systems can look like the branches of plants and trees

Hübler¹

2012

Complexity

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L -systems are extremely simple algorithms: visualize a turtle moving a distance d straight forward. At the end of its journey it produces two offspring, one branching of slightly to the left and the second branching off slightly to the right, and both traveling a 10% shorter distance. Once the offspring reach their destination, each creates two offspring which branch off slightly to the left and slightly to the right and move straight ahead a distance which is only 10% shorter than that of their parents. This process is repeated for seven generations. Figure 1 shows the trails of the turtles. The trails form a ramified structure, a turtle graph, which resembles the branching structure of trees and weeds. Figure 2 is an example for the branching pattern of a weed. Compared to the branching patterns of plants, turtle graphs appear too regular. However if the length of the trails of the offspring is randomized by 50% and some turtles produce only one offspring or none, then the turtle graphs a surprisingly similar to plant structures. Figure 1 contains a turtle graph where offspring are produced with a 90% probability. To illustrate that the code is very short and simple, we show a MatLab TM version in Table 1.The parameters of turtle graphs can be used to match the appearance of plants. The branching angle and the ratio of the trail length of the offspring and the parent are the genetic properties of plant species. The survival rate of the offspring depends on the general well-being of the plant determined by the climate and soil conditions. Weather, deseases, and other short term fluctuations influence the magnitude of the fluctuations of the trail length.The mathematics of deterministic turtle graphs is amazingly simple. The number of endpoints L 5 2 g , where g is the number of generations and the number of branch points is Y 5 2 g 21. For turtle graphs where the ratio of the trail length of offspring and parents r < 1, the distance from the start to each endpoint is R 5 d (1 2 r g )/(12r), never exceeds the value R 5 d/(12r) even for an unlimited number of generations. The height and the width have limiting values too. This means that the width and height of the plant never exceeds these limits even if the plant has an unlimited life span. However, little is known about the expectation values of the height and width and other quantities of probabilistic turtle graphs.The technical term for turtle graph is L-system, named after the Hungarian biologist Aristid Lindenmayer [1] or iterated function system [2]. They have been used to model plants for animated movies [3] and to analyze and model other complex data, such as music [4]. Hornby [5] uses graphs to model the modularity ALFRED HÜ BLERAlfred Hü bler is the Director of the

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Theories of complexity

2003

Self Cite

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Complexity scientists often express the need for the development of a theory of complexity. One of the major problems on the way toward such a theory is the lack of a generally agreed on definition of complexity. In this article it is proposed that, whatever definition one might one day agree on, contextuality and radical openness are essential features of complexity. Both properties are clarified by means of an example and implications for a future theory of complexity are discussed.

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Can agent-based models assist decisions on large-scale practical problems? A philosophical analysis

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 39 publications

References 17 publications

Verifying and Validating Simulations

Verifying and Validating Simulations

Probabilistic L‐systems can look like the branches of plants and trees

Theories of complexity

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