The Explanatory Potential of Artificial Societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Instead, artificial societies may sometimes provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from the standard kind of potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds of potential explanations differ, and how possible functional analyses can be appraised

Flight crew aiding for recovery from subsystem failures

Some of the conceptual issues associated with pilot aiding systems are discussed and an implementation of one component of such an aiding system is described. It is essential that the format and content of the information the aiding system presents to the crew be compatible with the crew's mental models of the task. It is proposed that in order to cooperate effectively, both the aiding system and the flight crew should have consistent information processing models, especially at the point of interface. A general information processing strategy, developed by Rasmussen, was selected to serve as the bridge between the human and aiding system's information processes. The development and implementation of a model-based situation assessment and response generation system for commercial transport aircraft are described. The current implementation is a prototype which concentrates on engine and control surface failure situations and consequent flight emergencies. The aiding system, termed Recovery Recommendation System (RECORS), uses a causal model of the relevant subset of the flight domain to simulate the effects of these failures and to generate appropriate responses, given the current aircraft state and the constraints of the current flight phase. Since detailed information about the aircraft state may not always be available, the model represents the domain at varying levels of abstraction and uses the less detailed abstraction levels to make inferences when exact information is not available. The structure of this model is described in detail

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

How do medical researchers make causal inferences?

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models

Measuring autonomy and emergence via Granger causality

Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness