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International audience The concept of power provides a unique analytical tool to study, understand and explain organized action set ups. The paper lists a series of examples derived from the research practice of its author. Such a perspective does not imply that this tool by definition makes sense only for theoretical agendas dealing with power and domination issues. The paper also explores some scientific enigmas that are still open for further inquiry

It is broadly assumed that political elites (e.g. party leaders) regularly rely on heuristics in their judgments or decision-making. In this article, I aim to bring together and discuss the scattered literature on this topic. To address the current conceptual unclarity, I discuss two traditions on heuristics: (1) the heuristics and biases (H&B) tradition pioneered by Kahneman and Tversky and (2) the fast and frugal heuristics (F&F) tradition pioneered by Gigerenzer et al. I propose to concentrate on two well-defined heuristics from the H&B tradition—availability and representativeness—to empirically assess when political elites rely on heuristics and thereby understand better their judgments and decisions. My review of existing studies supports the notion that political elites use the availability heuristic and possibly the representativeness one for making complex decisions under uncertainty. It also reveals that besides this, we still know relatively little about when political elites use which heuristic and with what effect(s). Therefore, I end by proposing an agenda for future research.

This paper looks into decision-making processes in the course of a crisis. In particular, it looks into how governments decide and use heuristics during crisis management, in order to transfer policies, as an attempt to reinforce or regain their legitimacy in unsettled times. It aims to understand how two institutional features, one intrinsic–problem uncertainty– and one extrinsic –ambiguity at the international level– affect decision-making. It develops a typology of policy transfer under these two dimensions.It examines the case of disappearances in Mexico, where Congress issued national legislation in a crisis generated after the disappearance of 43 students in 2014. The Law on Disappeared was a result of an explicit attempt to transfer international guidelines.Results show that, as long as the degree of uncertainty surrounding the problem is low, heuristics change according the degree of ambiguity at the international level: when they are clear, tallying occurs, while when they are ambiguous, one-reason heuristics would favor binding instrument.Furthermore, findings also point towards a nuanced understanding on the effect of a high degree of problem uncertainty on policy transfer. The research is relevant for understanding how, in crisis, decisions adapt based on context.

It is broadly assumed that political elites (e.g. party leaders) regularly rely on heuristics in their judgments or decision-making. In this article, I aim to bring together and discuss the scattered literature on this topic. To address the current conceptual unclarity, I discuss two traditions on heuristics: (1) the heuristics and biases (H&B) tradition pioneered by Kahneman and Tversky and (2) the fast and frugal heuristics (F&F) tradition pioneered by Gigerenzer et al. I propose to concentrate on two well-defined heuristics from the H&B tradition—availability and representativeness—to empirically assess when political elites rely on heuristics and thereby understand better their judgments and decisions. My review of existing studies supports the notion that political elites use the availability heuristic and possibly the representativeness one for making complex decisions under uncertainty. It also reveals that besides this, we still know relatively little about when political elites use which heuristic and with what effect(s). Therefore, I end by proposing an agenda for future research.

Meta-heuristics has a long tradition in computer science. During the past few years, different types of meta-heuristics, specially evolutionary algorithms got noticeable attention in dealing with real-world optimization problems. Recent advances in this field along with rapid development of high processing computers, make it possible to tackle various engineering optimization problems with relative ease, omitting the barrier of unknown global optimal solutions due to the complexity of the problems. Following this rapid advancements, scientific communities shifted their attention towards the development of novel algorithms and techniques to satisfy their need in optimization. Among different research areas, astrodynamics and space engineering witnessed many trends in evolutionary algorithms for various types of problems. By having a look at the amount of publications regarding the development of meta-heuristics in aerospace sciences, it can be seen that a high amount of efforts are dedicated to develop novel stochastic techniques and more specifically, innovative evolutionary algorithms on a variety of subjects. In the past decade, one of the challenging problems in space engineering, which is tackled mainly by novel evolutionary algorithms by the researchers in the aerospace community is spacecraft trajectory optimization. Spacecraft trajectory optimization problem can be simply described as the discovery of a space trajectory for satellites and space vehicles that satisfies some criteria. While a space vehicle travels in space to reach a destination, either around the Earth or any other celestial body, it is crucial to maintain or change its flight path precisely to reach the desired final destination. Such travels between space orbits, called orbital maneuvers, need to be accomplished, while minimizing some objectives such as fuel consumption or the transfer time. In the engineering point of view, spacecraft trajectory optimization can be described as a black-box optimization problem, which can be constrained or unconstrained, depending on the formulation of the problem. In order to clarify the main motivation of the research in this thesis, first, it is necessary to discuss the status of the current trends in the development of evolutionary algorithms and tackling spacecraft trajectory optimization problems. Over the past decade, numerous research are dedicated to these subjects, mainly from two groups of scientific communities. The first group is the space engineering community. Having an overall look into the publications confirms that the focus in the developed methods in this group is mainly regarding the mathematical modeling and numerical approaches in dealing with spacecraft trajectory optimization problems. The majority of the strategies interact with mixed concepts of semi-analytical methods, discretization, interpolation and approximation techniques. When it comes to optimization, usually traditional algorithms are utilized and less attention is paid to the algorithm development. In some cases, researchers tried to tune the algorithms and make them more efficient. However, their efforts are mainly based on try-and-error and repetitions rather than analyzing the landscape of the optimization problem. The second group is the computer science community. Unlike the first group, the majority of the efforts in the research from this group has been dedicated to algorithm development, rather than developing novel techniques and approaches in trajectory optimization such as interpolation and approximation techniques. Research in this group generally ends in very efficient and robust optimization algorithms with high performance. However, they failed to put their algorithms in challenge with complex real-world optimization problems, with novel ideas as their model and approach. Instead, usually the standard optimization benchmark problems are selected to verify the algorithm performance. In particular, when it comes to solve a spacecraft trajectory optimization problem, this group mainly treats the problem as a black-box with not much concentration on the mathematical model or the approximation techniques. Taking into account the two aforementioned research perspectives, it can be seen that there is a missing link between these two schemes in dealing with spacecraft trajectory optimization problems. On one hand, we can see noticeable advances in mathematical models and approximation techniques on this subject, but with no efforts on the optimization algorithms. On the other hand, we have newly developed evolutionary algorithms for black-box optimization problems, which do not take advantage of novel approaches to increase the efficiency of the optimization process. In other words, there seems to be a missing connection between the characteristics of the problem in spacecraft trajectory optimization, which controls the shape of the solution domain, and the algorithm components, which controls the efficiency of the optimization process. This missing connection motivated us in developing efficient meta-heuristics for solving spacecraft trajectory optimization problems. By having the knowledge about the type of space mission, the features of the orbital maneuver, the mathematical modeling of the system dynamics, and the features of the employed approximation techniques, it is possible to adapt the performance of the algorithms. Knowing these features of the spacecraft trajectory optimization problem, the shape of the solution domain can be realized. In other words, it is possible to see how sensitive the problem is relative to each of its feature. This information can be used to develop efficient optimization algorithms with adaptive mechanisms, which take advantage of the features of the problem to conduct the optimization process toward better solutions. Such flexible adaptiveness, makes the algorithm robust to any changes of the space mission features. Therefore, within the perspective of space system design, the developed algorithms will be useful tools for obtaining optimal or near-optimal transfer trajectories within the conceptual and preliminary design of a spacecraft for a space mission. Having this motivation, the main goal in this research was the development of efficient meta-heuristics for spacecraft trajectory optimization. Regarding the type of the problem, we focused on space rendezvous problems, which covers the majority of orbital maneuvers, including long-range and short-range space rendezvous. Also, regarding the meta-heuristics, we concentrated mainly on evolutionary algorithms based on probabilistic modeling and hybridization. Following the research, two algorithms have been developed. First, a hybrid self adaptive evolutionary algorithm has been developed for multi-impulse long-range space rendezvous problems. The algorithm is a hybrid method, combined with auto-tuning techniques and an individual refinement procedure based on probabilistic distribution. Then, for the short-range space rendezvous trajectory optimization problems, an estimation of distribution algorithm with feasibility conserving mechanisms for constrained continuous optimization is developed. The proposed mechanisms implement seeding, learning and mapping methods within the optimization process. They include mixtures of probabilistic models, outlier detection algorithms and some heuristic techniques within the mapping process. Parallel to the development of algorithms, a simulation software is also developed as a complementary application. This tool is designed for visualization of the obtained results from the experiments in this research. It has been used mainly to obtain high-quality illustrations while simulating the trajectory of the spacecraft within the orbital maneuvers. ; La Caixa TIN2016-78365R PID2019-1064536A-I00 Basque Government consolidated groups 2019-2021 IT1244-19

With their inherent flexibility and robustness to change, the decentralised interconnected knowledge graphs that lie at the heart of semantic web technologies are ideally suited for the challenges of converting the messy, often incomplete, and internally heterogeneous datasets of the Humanities into machine processable data. Although a matter of some debate, the reuse and adoption of known ontologies, schema, and taxonomies across disparate projects across the Arts, Humanities, and Social Sciences landscape has been steadily increasing over the last decade in particular. This talk will describe the practical approaches and heuristics of such Linked Data projects, commenting on the effect of political, institutional, and socio-cultural factors in their planning, implementation, and evaluation.

The article identifies foundational principles of the structural analysis of economic, political and social systems, and explores their bearing on the study of structural transformations. In doing so, it delves especially into the conceptual resources provided by strands of economic theory. Particular importance is given to the relative positions of socio-economic groups, productive sectors, and institutions; the relative invariance of certain patterns of interdependence vis-à-vis others; and the view of economic dynamics as structural transformation subject to the condition of relative invariance. The article goes on to introduce and discuss a collection of papers that explore the relationship between structures and transformations in economic, political and social systems. A fundamental theme is the relationship between the range of transformations made possible by structures and the individual or collective actions taking place within those structures, which lead to some structural transformations instead of others.

In this note we argue that heuristics should not only be seen as the tricks and moves that the social scientist uses in the hope of making a discovery. Heuristics also represents a form of acting on oneself, in the process of which a new knowledge or discourse emerges that can be studied and discussed. Some parallels are drawn between this type of process and the emergence of ethical action or "ethical work", as described by Michel Foucault in The History of Sexuality (1985). The new field of knowledge that is today being created in social science does not only consist of heuristics but also of many other related types of thinking and their products, all of which are referred to in sociology as theorizing. Theorizing complements traditional theory as well as the history of theory through its focus on how theory is actually being used and created - what has been called "theory work". As with all new forms of knowledge, theorizing will face being scrutinized, neutralized and possibly taken over. How this struggle will end depends among other things on the depth to which theorizing can shape the selves of social scientists. The tradition of theorizing, the reader is reminded, ultimately draws on Kant's efforts to democratize thinking and assist the common person.

Advances in cyber capabilities continue to cause apprehension among the public. With states engaging in cyber operations in pursuit of its perceived strategic utility, it is unsurprising that images of a "Cyber Pearl Harbor" remain appealing. It is crucial to note, however, that the offensive action in cyberspace has only had limited success over the past decade. It is estimated that less than 5% of these have achieved their stated political or strategic objectives. Moreover, only five states are thought to have the capabilities to inflict or threaten substantial damage. Consequently, this raises the question of what accounts for the continued sense of dread in cyberspace. The article posits that this dread results from the inappropriate use of cognitive shortcuts or heuristics. The findings herein suggest that the lack of experience in dealing with cyber operations encourages uncertainty, which motivates decision-makers to base their judgements on pre-existing, and possibly incorrect, conceptions of cyberspace. In response, the article segues into potential solutions that can mitigate unsubstantiated dread towards cyberspace by peering into the role that attributes at the organizational level can play in tempering the position of individuals. The suggested considerations are rooted in the interactions between the micro and macro level processes in forming judgments, sensemaking, and ultimately, mobilizing actions.

Advances in cyber capabilities continue to cause apprehension among the public. With states engaging in cyber operations in pursuit of its perceived strategic utility, it is unsurprising that images of a "Cyber Pearl Harbor" remain appealing. It is crucial to note, however, that the offensive action in cyberspace has only had limited success over the past decade. It is estimated that less than 5% of these have achieved their stated political or strategic objectives. Moreover, only five states are thought to have the capabilities to inflict or threaten substantial damage. Consequently, this raises the question of what accounts for the continued sense of dread in cyberspace. The article posits that this dread results from the inappropriate use of cognitive shortcuts or heuristics. The findings herein suggest that the lack of experience in dealing with cyber operations encourages uncertainty, which motivates decision-makers to base their judgements on pre-existing, and possibly incorrect, conceptions of cyberspace. In response, the article segues into potential solutions that can mitigate unsubstantiated dread towards cyberspace by peering into the role that attributes at the organizational level can play in tempering the position of individuals. The suggested considerations are rooted in the interactions between the micro and macro level processes in forming judgments, sensemaking, and ultimately, mobilizing actions.

This paper examines the role of simplified heuristics in the formation of preferences for public goods. Political scientists have suggested that voters use simplified heuristics based on the positions of familiar parties to infer how a proposed policy will affect them and to cast a vote in line with their interests and values. Here, we use a two-stage field-survey experiment to investigate how knowledge of party positions affects policy choices. We followed standard procedures in developing an attribute-based choice experiment on alternative land-use policies in Switzerland. In contrast to the usual formulation, however, the hypothetical costs of the proposed policies were formulated as a percentage change in taxes. The benefit of this formulation relative to the usual absolute money amounts is that the credibility of the (hypothetical) costs for respondents does not depend on respondent income. Furthermore, the formulation allowed us to solicit party positions on the proposed policies. Six out of eight contacted parties provided their positions. We then conducted a split-sample mail survey where we included a table of the party positions with a subsample of the questionnaires. We report six main experimental results. (1) The response rate of the survey was unaffected by the party positions. (2) The proportion of no-choice answers was decreased by forty percent relative to the control. (3) The party information significantly affected the choices directly and in interaction with respondents' general attitudes towards public spending for nature and landscape conservation and thus affected the way how individuals mapped from general attitudes to preferences for specific policies. (4) The information interacted with educational level in only eight out of forty choice sets, suggesting that even the more educated relied on simplified heuristics. (5) Respondents who knew the party positions were more sensitive to the tax attribute. (6) For respondents with medium and higher tax bills, the resulting willingness-to-pay estimates were decreased by a factor of two to ten relative to the control. These findings suggest that the party information helped the respondents to articulate more consistent preferences than in the treatment without the party information.

Air refueling is an integral part of U.S. air power across a wide range of military operations. It is an essential capability in the conduct of air operations worldwide and is especially important when overseas basing is limited or not available. The planning, tasking, and scheduling of aerial refueling require solution of two major problems: assigning and scheduling of tankers to refueling points and efficiently assigning crews to each tanker. To address the scheduling of tankers, Wiley (2001) developed an efficient tabu search approach. Combs (2002) developed another tabu search approach to assign crews to tankers. This research combines the two scheduling heuristics so that the tanker schedules generated by the tanker scheduling heuristics can feed the crew scheduling heuristic.

There is a large literature on social epistemology, some of which is concerned with expert knowledge. Formal representations of the aggregation of decisions, estimates, and the like play a larger role in these discussions. Yet these discussions are neither sufficiently social nor epistemic. The assumptions minimize the role of knowledge, and often assume independence between observers. This paper presents a more naturalistic approach, which appeals to a model of epistemic gain from others, as mutual consilience—a genuinely social notion of epistemology. Using the example of Michael Polanyi's account of science as an illustration, it introduces the notion of double heuristics: that individuals, each with their own heuristics, each with cognitive biases and limitations, are aggregated by a decision procedure, like voting, and this second order procedure produces its own heuristic, with its own cognitive biases and limitations. An example might be the limited ability of democracies to assimilate expert knowledge.

There is a large literature on social epistemology, some of which is concerned with expert knowledge. Formal representations of the aggregation of decisions, estimates, and the like play a larger role in these discussions. Yet these discussions are neither sufficiently social nor epistemic. The assumptions minimize the role of knowledge, and often assume independence between observers. This paper presents a more naturalistic approach, which appeals to a model of epistemic gain from others, as mutual consilience—a genuinely social notion of epistemology. Using the example of Michael Polanyi's account of science as an illustration, it introduces the notion of double heuristics: that individuals, each with their own heuristics, each with cognitive biases and limitations, are aggregated by a decision procedure, like voting, and this second order procedure produces its own heuristic, with its own cognitive biases and limitations. An example might be the limited ability of democracies to assimilate expert knowledge.

2012 Summer. ; Includes bibliographical references. ; On the modern battlefield, cordon and search missions (a.k.a. village searches) are conducted daily. Creating resource allocations that assign different types of search teams (e.g., soldiers, robots, unmanned aerial vehicles, military working dogs) to target buildings of various sizes is difficult and time consuming in the static planning environment. Efficiently and effectively creating resource allocations when needed during mission execution (a dynamic environment) is even more challenging. There are currently no automated means to create these static and dynamic resource allocations for military use. Military planners create village search plans using reference tables in Field Manuals and personal experience. These manual methods are time consuming and the quality of the plans produced are unpredictable and not quantifiable. This work creates a mathematical model of the village search environment, and proposes static and dynamic resource allocation heuristics using robustness concepts. The result is a mission plan that is resilient against uncertainty in the environment and that saves valuable time for military planning staff.