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Theories of policy responsiveness assume that political decision-makers can rationally interpret information about voters' likely reactions, but can we be sure of this? Political decision-makers face considerable time and information constraints, which are the optimal conditions for displaying decision-making biases—deviations from comprehensive rationality. Recent research has shown that when evaluating policies, political decision-makers display biases related to heuristics—cognitive rules of thumb that facilitate judgments and decision-making—when evaluating policies. It is thus likely that they also rely on heuristics in other situations, such as when forming judgments of voters' likely reactions. But what types of heuristics do political decision-makers use in such judgments, and do these heuristics contribute to misjudgements of voters' reactions? Existing research does not answer these crucial questions. To address this lacuna, we first present illustrative evidence of how biases related to heuristics contributed to misjudgements about voters' reactions in two policy decisions by UK governments. Then, we use this evidence to develop a research agenda that aims to further our understanding of when political decision-makers rely on heuristics and the effects thereof. Such an agenda will contribute to the literature on policy responsiveness.

ABSTRACTThis paper proposes a class of surrogate constraint heuristics for obtaining approximate, near optimal solutions to integer programming problems. These heuristics are based on a simple framework that illuminates the character of several earlier heuristic proposals and provides a variety of new alternatives. The paper also proposes additional heuristics that can be used either to supplement the surrogate constraint procedures or to provide independent solution strategies. Preliminary computational results are reported for applying one of these alternatives to a class of nonlinear generalized set covering problems involving approximately 100 constraints and 300–500 integer variables. The solutions obtained by the tested procedure had objective function values twice as good as values obtained by standard approaches (e.g., reducing the best objective function values of other methods from 85 to 40 on the average. Total solution time for the tested procedure ranged from ten to twenty seconds on the CDC 6600.

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.

Lay perceptions of risk appear rooted more in heuristics than in reason. A major concern of the risk regulation literature is that such "error‐strewn" perceptions may be replicated in policy, as governments respond to the (mis)fears of the citizenry. This has led many to advocate a relatively technocratic approach to regulating risk, characterized by high reliance on formal risk and cost‐benefit analysis. However, through two studies of chemicals regulation, we show that the formal assessment of risk is pervaded by its own set of heuristics. These include rules to categorize potential threats, define what constitutes valid data, guide causal inference, and to select and apply formal models. Some of these heuristics lay claim to theoretical or empirical justifications, others are more back‐of‐the‐envelope calculations, while still more purport not to reflect some truth but simply to constrain discretion or perform a desk‐clearing function. These heuristics can be understood as a way of authenticating or formalizing risk assessment as a scientific practice, representing a series of rules for bounding problems, collecting data, and interpreting evidence (a methodology). Heuristics are indispensable elements of induction. And so they are not problematic per se, but they can become so when treated as laws rather than as contingent and provisional rules. Pitfalls include the potential for systematic error, masking uncertainties, strategic manipulation, and entrenchment. Our central claim is that by studying the rules of risk assessment qua rules, we develop a novel representation of the methods, conventions, and biases of the prior art.

What is rationality and what is the evidence for it? -- The case for bounded rationality -- Behavioral models of heuristics-based choice -- Kahneman and Tversky's research program on heuristics and biases (HBP) -- The fast and frugal heuristics research program (FFP) -- Philosophical foundations -- Optimal taxation and regulation in behavioral economics -- Libertarian paternalism in theory -- Libertarian paternalism in practice.

PurposeCustomer centricity has evolved into a success factor for many companies, requiring all corporate activities – including business processes – to be aligned with customer needs. With most existing approaches to business process (re-)design focusing on process efficiency, customers are often treated as second-class citizens. Despite emergent research on customer process management, there is a lack of guidance on how to design customer-centric business processes.Design/methodology/approachThe authors conducted a structured literature review and analyzed companies awarded for outstanding customer centricity to compile design heuristics for customer-centric business processes. The authors iteratively validated and refined these heuristics with experts from academia and industry. Finally, the heuristics was grouped according to their expected impact on interaction capabilities to enable their prioritization in specific settings.FindingsThe authors proposed 15 expert-approved and literature-backed design heuristics for customer-centric business processes together with real-world examples. The heuristics aim at increasing customer satisfaction with interaction-intensive core processes, which is an important driver of corporate success.Originality/valueThe design heuristics complement existing efficiency-centered (re-)design heuristics. They reflect cognitive shortcuts that support process analysts in the generation of innovative ideas during process (re-)design. The heuristics also add to customer process management and help put customer centricity into practice.

Moral assessment implies ascribing a status of morally wrong, good, etc. (target attribute) to an act. Such an assessment is made on the basis of information about other attributes of the act, including its compliance with the norm, consequences, opinions of others about it, etc. These attributes may be morally relevant (those attributes that an individual could, in the case of rational analysis, recognize as a direct basis for moral assessment) and morally irrelevant (those which would not be recognized in such a status). A comprehensive moral assessment of the target attribute is an assessment based on all morally relevant attributes. A heuristic assessment is based only on a part of morally relevant attributes or based on morally irrelevant attributes. This difference between moral heuristics became the basis for dividing them into two types. Heuristics of the first type implies a simplified assessing of the target attribute based on partial information about morally relevant attributes of an act. The heuristic of the second type operates through a process of attribute substitution when irrelevant attributes are used to assess the target attribute.

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.

Abstract
With theories of heuristics and decision-making ecology as our analytical lens, we examined social work practitioners' use of heuristics (rules-of-thumb) as a response to real-world situations, emphasising the legal framework and how the work was organised. The data comprised eleven exploratory and follow-up group interviews with thirty-nine social workers and team leaders, observations of 108 decisions in child cases during eighteen meetings, and an analysis of fourteen case files in two Danish municipalities. Open, interpretative coding was used within a systemic approach, using Fish et al. (2008). Three heuristic rules guided decisions in all cases: (i) Form and maintain the first impression. (ii) When in doubt or disagreement, wait. (iii) Avoid parents' resistance. Key takeaways are that these heuristics help social workers and managers and are connected to satisficing strategies as a natural response to legal and organisational factors. However, the heuristics can also lead to a lack of transparency, delayed interventions and other kinds of bias. We point out the need for understanding such patterns through an extended research to facilitate better and timely feedback to practitioners on their decisions.