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Attractor-Based Fitness Landscapes for Computational Decision Search

机译：基于吸引人的健身景观，用于计算决策搜索

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摘要

Managerial decision making involves searches for alternative courses of action, including searches for technological innovations. A substantial stream of computational work on managerial decision making has been based on search using Kauffman's NK landscape model, which represents fitness or payoff values to a discrete set of binary strings. In this paper, we propose a new method for landscape generation, the method of superposition of attractors, in which the fitness landscape is continuous. We introduce the attractor-based (AB) fitness landscape model, the core model based on this method, with parameters specifying the number of attractors and the steepnesses and heights of landscape peaks in the neighborhoods of attractors. We then describe search using this model, consider issues in implementing the search process, and provide an example of applying the model to studying exploration and exploitation. Next, we compare the AB and NK landscape approaches and identify some advantages and disadvantages of the AB approach relative to the NK approach. Advantages of the AB model include more control over the shape of the fitness landscape, applicability to outcomes not arising from intraorganizational interdependence, and visualization. We then consider customizations and generalizations of the model, including applications to coordinated exploration and resource partitioning processes.
机译：管理决策涉及寻找替代性行动方案，包括寻找技术创新。有关管理决策的大量计算工作都是基于使用考夫曼（Kauffman）的NK景观模型进行搜索的结果，该模型表示离散集二进制字符串的适用性或收益值。在本文中，我们提出了一种新的景观生成方法，即吸引人叠加的方法，其中健身景观是连续的。我们介绍了基于吸引器的（AB）健身景观模型，它是基于此方法的核心模型，其参数指定了吸引器的数量以及吸引器附近景观峰的陡度和高度。然后，我们使用该模型描述搜索，考虑实现搜索过程中的问题，并提供将模型应用到研究勘探和开发中的示例。接下来，我们比较AB和NK景观方法，并确定AB方法相对于NK方法的优缺点。 AB模型的优势包括对健身景观形状的更多控制，对非组织内相互依赖性产生的结果的适用性以及可视化。然后，我们考虑模型的自定义和概括，包括协调探索和资源划分过程的应用程序。

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