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In recent years, the price of electricity in Japan has increased, and then manufacturing industries are required to implement production plan in consideration of the electric power costs. In response to this demand, production scheduling has to be implemented in consideration of several decision criteria such as productivity and electric power costs. In this paper, we propose a production scheduling with satisficing trade-off method in order to maintain productivity and reduce electricity costs, and evaluate the effectiveness of the proposed method.

While Specialist Firearms Commanders may be designated as a ‘Strategic Commander’ or ‘Tactical Commander’ within an Operation, they carry out a strategic leadership role, having the responsibility to translate organisational strategy into effective operational performance within a complex intelligence led multi-agency operation.

In terms of urban and extra-urban driving environments, one of the most common phenomena of the last two decades has been the proliferation of roundabouts, which in some cases have replaced the traffic light systems and, in other cases, the horizontal signs typical of road junctions. In the management of traffic flows, roundabouts are often regarded as satisficing solutions, that is, they increase average traffic speed while reducing the number of road accidents satisfactorily. Besides from the role played by physical, structural features, deeply investigated by designers, engineers, and other scientists alike, the social outcome of roundabouts largely depends on the behaviour they are capable of inducing in motorists—and drivers in general—as well as on social cognition. It is very likely, though, that drivers’ behaviour differs across individual agents because of exogenous factors, such as cultural and local traditions, education, family upbringing, and other social backgrounds and cognitive relationships. Here, we study the behaviour of motorists found in metadata, that is, data collected in other studies, with a particular focus on Italy and the United States. The basic idea is to compare the decisions made by motorists at urban and extra-urban roundabouts to those made by economic agents interacting in an Ultimatum Game with discrete offers. Nonetheless, the novelty and relevance of this work lie primarily in the emergence of roundabouts theoretically understood as bargaining environments examined through the Ultimatum Game. Analysing some metadata, we find that motorists might behave as rational agents, although fundamental heuristic factors still need to be thoroughly investigated in future work. This approach allows us both to add very preliminary findings to the vast economic literature dealing with the experimental evidence about Subgame Perfect Nash Equilibria in Ultimatum Games and, above all, to develop a research project based on large-scale natural experimentation and data mining, from classical to modern behavioural economics, there and back again.

The present research work discusses a possible formalisation in the Matita Interactive Theorem Prover, and initial results on the development of an decisional Turing Machine within Herbert Simon’s bounded rationality and satisficing decision theoretical framework. Specifically, it implements the decision procedure in static and dynamic ways. The present research has the purpose of being a potential preliminary step towards the creation of a future investigation the Reverse Complexity Program immersed in the classical behavioural economic framing, which would uncover part of the real mechanisms that an economic agent might face during the decision process.

Reducing the search space is one of the challenges in reinforcement learning. One of the satisficing reinforcement learning algorithms, commonly known as RS+GRC, reduces large search space by setting an aspiration level. However, a lag between policy evaluation and improvement, due to policy feedback, prevents proper exploration. Therefore, we propose an eligibility trace-based RS(\(\lambda \)) method, which eliminated the lag. We demonstrated that RS(\(\lambda \)) exhibited efficient learning toward behavior policy-based satisfaction.

Humans, by nature, tend to act like pack animals and participate in groupthink. We are also reluctant to disagree with our supervisors for fear of retribution or being criticized for not being a team player. Further, if we become invested in a plan, we are loath to let go of it because we view our time and effort as having been wasted. We tend to engage in satisficing when all our presumed criteria for action have been met. Red Teaming teaches us to diverge before we converge. It permits us to remain agile so we can change quickly when the anticipated future changes.

As part of a larger project looking at the design of future classrooms, we delivered a survey on technology use to school pupils in the United Kingdom and in Indonesia. Whilst seeking to discover opinions from the pupils in both of these locations, we also had an interest in exploring what the results might also tell us in regard to future design sessions. Over 300 pupils completed the survey, and the results show significant differences and similarities across the two groups. It can be postulated that some of the differences are caused by the students’ behaviours around satisficing, and some are a result of experiences. We reflect on implications for the design of design sessions around words, worlds and freedom.

Many real-world questions that professionals face occur in complex, dynamic environments where information is often sparse, e.g., clinical decision-making, cyber security, stock market prediction. In many cases, problems are open-ended without a single or optimal solution. Providing effective training in these ill-defined environments presents an important challenge for educators. Using the healthcare professions as a case study, this chapter outlines a framework for knowledge acquisition in the professions. It argues that dynamic, adaptive criteria must be identified based on educational theory, psychometric techniques, and properties of expert performance. From this approach, educators must develop assessment criteria that satisfice, framing problems in terms of an order of difficulty relative to the learner’s current level of comprehension. This reflects a quantitative approach to the zone-of-proximal development (ZPD), that removes the upper-bound for knowledge acquisition. In health professions education, this approach can be used to create a competency profile. Finally, given that professional practices often focus on the efficient use of resources, I argue that measures of the speed-accuracy trade-off should be used to assess expert performance.

This is an extension from a selected paper from JSAI2019. Social learning is crucial in acquisition of the intelligent behaviors of humans and many kinds of animals, as it makes behavior learning far more efficient than pure trial-and-error. In imitation learning, a representative form of social learning, the agent observes specific action-state pair sequences produced by another agent (expert) and reflect them into its own action. One of its implementations in reinforcement learning is the inverse reinforcement learning. We propose another form of social learning, emulation learning, which requires much less information from another agent (pioneer). In emulation learning, the agent is given only a certain level of achievement by another agent, or a record. In this study, we implement emulation learning in the reinforcement learning setting by applying a model of satisficing action policy. We show that the emulation learning algorithm works well both in stationary and non-stationary reinforcement learning tasks, breaking the often observed trade-off like relationship between efficiency and flexibility.

We examine the satisficing respondent behavior and cognitive load of the participants in particular web survey interfaces applying automatic forwarding (AF) or manual forwarding (MF) in order to forward respondents to the next item. We create a theoretical framework based on the Cognitive Load theory (CLT), Cognitive Theory of Multimedia Learning (CTML) and Survey Satisficing Theory taken also into account the latest findings of cognitive neuroscience. We develop a new method in order to measure satisficing responding in web surveys. We argue that the cognitive response process in web surveys should be interpreted starting at the level of sensory memory instead of at the level of working memory. This approach allows researchers to analyze an accumulation of cognitive load across the questionnaire based on observed or hypothesized eye-movements taken into account the interface design of the web survey. We find MF reducing both average item level response times as well as the standard deviation of item-level response times. This suggests support for our hypothesis that the MF interface as a more complex design including previous and next buttons increases satisficing responding generating also the higher total cognitive load of respondents. The findings reinforce the view in HCI that reducing the complexity of interfaces and the presence of extraneous elements reduces cognitive load and facilitates the concentration of cognitive resources on the task at hand. It should be noted that the evidence is based on a relatively short survey among university students. Replication in other settings is recommended.

This final chapter sums up the lessons that can be learned from this book. It discusses the fact that, in a world as messy and chaotic as ours, anthropologists must seek theories that explore societies’ workings by looking at the local, experiential, and practical—explaining societies as the products of individuals working together. Empathy, satisficing, and shared problem-solving are essential tools for our subjects, and, for anthropologists, figuring out how to deal with difference is every bit as important as understanding conformity. Cultural ecosystems, then, are laboratories for experimenting, exploring, and learning, as well as the places where people teach one another about what they have found.

Humans have been making decisions for tens of thousands of years. Over the years they have developed different ‘ways’ of making decisions. Scholars have developed theories to model human decision-making based on each ‘way’ of making decisions. In this chapter we discuss the pros and cons of several commonly used ways of making decisions. They have been found useful, although they also have their limitations. We discuss the satisficing approach and contrast it with the optimizing approach, the lexicographic model, the compensatory models, ‘Elimination By Aspects’-model, and prospect theory.

People employ both intuitive and deliberate thinking when making critical decisions. Chapter 4 described how our intuitions give rise to cognitive biases that impair critical decision-making and open the door to the Law of Unintended Consequences (LUC). Robert K. Merton also identified constraints on deliberate thinking as equally important contributors to LUC. This chapter examines Herbert Simon’s pivotal cognitive scientific research on these bounds on human rationality. Section 5.1 provides a brief sketch of Simon’s accomplishments. Section 5.2 introduces Simon’s concept of bounded rationality and contrasts it with Tversky and Kahneman’s research on cognitive biases. Sections 5.3 through 5.5 unpack the key elements of Simon’s research: his critique of rational choice theory, his cognitive model for deliberate (System 2) thinking, and his cognitive model of how people reason in order to make critical decisions. Section 5.6 recaps the significance of Simon’s research.

The phenomenon of immigrant population innumeracy has drawn considerable attention from social researchers over the past 15 years. This tendency to overestimate the size of foreign born populations has understandably generated concern about the consequences of faultily perceiving demographic realities. Indeed this ignorance is often associated with anti-immigrant policy positions related to the halting of immigration, the deportation of foreigners, or the rescinding of immigrants’ rights. However, largely omitted from the innumeracy research is consideration of those who do not provide an estimate. Like overestimators and underestimators, those responding with “don’t know” also display ignorance about a demographic reality. But is this ignorance meaningful? The current study considers this possibility through a detailed examination “don’t know” responses. Four general questions guide the analysis. First, what is the extent of “don’t know” regarding immigrant population size estimates across nations? The analysis combines data from four international surveys with similar size estimate prompts to document the extent across Europe and North America. Second, how can we characterize the “don’t knows”? The analysis examines data from the 2014 European Social Survey (ESS) to determine what factors make respondents more or less likely to provide no estimate across 14 countries. These multivariate models include several independent variables related to cognitive availability, negative affect, survey satisficing, and demographic factors, which are hypothesized to predict “don’t know” responses. The results reveal that providing no estimate is much more than simple random ignorance. Third, are there any consequences to “don’t know” responses? This multivariate analysis considers whether providing no estimate makes one more likely to support hypothetical policies limiting the flow of immigrants into their country. Fourth, what should future researchers do with the “don’t knows”? This final analysis re-estimates the previous results using two common strategies for handling missing observations—listwise deletion and multiple imputation—with the goal of determining how the results would differ if the “don’t know” responses are treated as a nuisance. This study concludes with recommendations for future research.

This chapter discusses why and how a computational cognitive model that captures the broader set of processes of information search is important and useful. The first reason why this can be useful is when information search is only one component of a broader task. A better understanding of the broader process of information search can lead to better metrics of relevance that are specific to the broader task in which the user is engaged. The second reason is that it helps to develop better personalized tools that are more compatible with the individual users as they search information for different purposes. Two examples of such computational cognitive models are presented. The first model, SNIF-ACT, demonstrates the value of adopting a theory-based mechanism, called the Bayesian satisficing mechanism (BSM), that selects information search strategies based on ongoing assessment of the information scent cues encountered by a user as he or she navigates across Web pages. The second model, ESL, tracks both learning of knowledge structures and search behavior in a social tagging system over a period of eight weeks as they continuously search for Web documents. These computational cognitive models generate explicit predictions on what users will do when they interact with different information retrieval systems for different tasks in different contexts. Computational cognitive models therefore complement existing computational techniques that aim to improve information or document retrieval. At the same time, they allow researchers to develop and test unified theories of information search by integrating the vast literature on information search behavior in different contexts.

Technology use, adoption and adaptation have been discussed extensively in existing scholarly works but scant consideration is given to technology adoption, adaptation and appropriation. This chapter endeavours to address this gap in the literature by conducting a critical review of the scholarly works to examine the underlying antecedents and discrete adaptation behaviour (the entirety of technology adoption, adaptation and appropriation). The findings of this review reveal that such entirety has the following key underlying joint attitudinal cognitive (perceived opportunity, perceived relative advantage, perceived social influence, perceived control) and affective (enjoyment, self-enhancement, threat, fear and trust) components as antecedents to interaction and leading to discrete adaptation and appropriation behaviour of exploration to maximise and exploitation to satisfice technology benefits on the one hand and exploration to revert from technology to complete abandoning of technology on the other. The conceptual underpinning presented and analysed in this chapter advances the scholarship of technology adoption, adaptation and appropriation in entirety and provides useful direction for future empirical research for both academics and practitioners.

An LMU is a component that supports the topside of the offshore plant, which is composed of elastomeric bearing and steel plate. One of the essential design requirements of the LMU is related to the stiffness. The structural design requirement is that the reaction force of the LMU should be designed as close to linear as possible. For satisfying this design requirement, this study applied the hybrid metamodel-based optimization technique and the satisficing trade-off method. In this optimization process, the design variables are defined as the parameters included in the LMU, and the multiobjective function is made. Also, the suggested optimum is compared with those by the weighting method.

This chapter investigates the negotiations on Montenegro’s independence in 2006. The author shows that many of the voting thresholds proposed by the two camps (unionists and pro-independents) during their negotiations about the independence referendum were salient or focal. Arguing that focal point solutions are often suboptimal to both parties, he demonstrates that they offer important opportunities to mediators. Focal points can induce the parties toward the adoption of a satisficing rather than a maximizing strategy conductive to the reaching of an agreement that both parties can live with. This is the case even when the mediators are neither unbiased nor indifferent to the outcome of the negotiations.

Systematic errors bias measurements. Obviously, cheating is a biasing factor. Answer copying is a type of cheating, which can be detected by answer-copying indices. Satisficing is responding to items with less than optimal efforts. It is detectable by building instructional manipulation checks or bogus items into the test. A response style is a typical way of taking a test. Plodding is a style where test takers start slowly and take much time to answer items. It is counteracted by informing test takers on the amount of time and number of items that remain. Fumbling is a style where test takers start rather panicky. It cannot be prevented, but testing should be less panic arousing as possible. The extremity and midpoint styles are the tendencies to choose extreme and midpoint answer categories, respectively. Acquiescence and dissentience are tendencies of yea- and nay-saying, respectively, and are detectable if the test is balanced (i.e., has about equal numbers of indicative and contra-indicative items). Satisficing and fumbling yield aberrant response patterns and acquiescence and dissentience aberrant response patterns at balanced tests that are detectable by person-fit indices. Item nonresponse is the tendency to skip items. It is reduced by computerized test administration and re-approaching test takers who omitted items. These styles and tendencies are sometimes person characteristics that are of interest of their own. Deleting test takers who show styles or tendencies yields missing data that have to be handled in the analysis of the data (see Chap. 16).

Murphy investigates the unique relationship between the actor and the instructor in Lecoq pedagogy. She explains how Lecoq’s via negativa intersects with his essentialist rhetoric and practices to forge the actor-creator. This chapter introduces the concept of satisficing to understand how Lecoq’s version of via negativa empowers the actor-creator to make choices that suit particular theatrical contexts. Murphy uses Hans Jonas’s notion of needful freedom to explain how freedom and constraint shape the emergence of the actor-creator. She addresses Lecoq’s essentialist rhetoric and practices by not only marking the ways that Lecoq destabilizes them, but also introducing how an enactive view reveals the necessity of creating pedagogical paradoxes to forge an empowered creator.

The authors develop a model for multi-criteria evaluation of big data within organisations concerned with the impact of an ad exposure on online consumption behaviour. The model has been structured to help organisations make decisions in order to improve the business knowledge and understanding on big data and, specifically, heterogeneous big data. The model accommodates a multilevel structure of data with a modular system that can be used both to automatically analyse data and to produce helpful insights for decision-making. This modular system and its modules, indeed, implement artificial intelligent algorithms such as neural networks and genetic algorithms. To develop the model, therefore, a prototype has been built as proof-of-concept using a marketing automation software that collects data from several sources (public social and editorial media content) and stores them into a large database so as the data can be analysed and used to implement business model innovations. In this regard, the authors are conducting a natural experiment - which has yet to be completed - to show that the model can provide useful insights as well as hints to help decision-makers take further account of the most ‘satisficing’ decisions among alternative courses of action.

Premature closure is the mistake of accepting a diagnosis before it has been fully verified. Its consequences are reflected in the maxim: “When the diagnosis is made, the thinking stops.” It is a powerful factor accounting for a high proportion of missed diagnoses. Several specific errors are discussed here. There are; the anchoring bias, a form of the anchoring bias called Occam’s error, the availability bias, search satisficing, the ying-yang error, diagnosis momentum, triage cueing, and unpacking failure. The last section in this chapter chapter will discuss the inverse of premature closure, the failure-to-close error.

Conventional wisdom holds that goals drive behavior and measured goals lead to results. The preeminent goals in business research revolve around financial performance. However, recently, more attention has been paid to non-financial goals, such as applied in the balanced scorecard. Family business research has also taken special note of goals, as family influence in business typically enhances the salience of non-financial goals that must be accommodated with more materialistic, financial objectives. For this and other reasons, goals are challenging to study in private family firms where goals reflect desired outcomes for family and business, and relative to publicly traded entities, owning-families have great freedom in goal selection, resulting in higher levels of goal idiosyncrasy and heterogeneity. To further the discussion of private family business goals, this chapter provides a concise review of the extant literature on family business goals, imparts relationships between financial and non-financial goals, and expounds processes of family business goal formation.

In contrast to the relative abundance of conceptualisations of “information literacy”, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

Convergence to an optimal policy using model-based reinforcement learning can require significant exploration of the environment. In some settings such exploration is costly or even impossible, such as in cases where simulators are not available, or where there are prohibitively large state spaces. In this paper we examine the use of advice to guide the search for an optimal policy. To this end we propose a rich language for providing advice to a reinforcement learning agent. Unlike constraints which potentially eliminate optimal policies, advice offers guidance for the exploration, while preserving the guarantee of convergence to an optimal policy. Experimental results on deterministic grid worlds demonstrate the potential for good advice to reduce the amount of exploration required to learn a satisficing or optimal policy, while maintaining robustness in the face of incomplete or misleading advice.

In order to achieve a compromised solution for a multi-objective linear programming with fuzzy right hand sides, Tchebycheff norm and a new approach based on \( \alpha \)-cut is suggested to minimize the distance from the current estimate of the objective values from the ideal point. Since the obtained solutions by the Tchebycheff approach are weakly efficient for multi-objective problems. Hence, an augmented weighted Tchebycheff norm has been proposed. Here, the satisficing tradeoff algorithm is used to solve the augmented weighted Tchebycheff problems. Since the supplier selection problem is usually a multi-objective problem, the augmented weighted Tchebycheff method is applied for obtaining its solutions.

In Chap. 3 we look at motivation—the things which inspire us to create and excel. We turn Maslow on its head and consider whether physiological needs are more important, or just easier to acquire, and consider whether there really is as much risk in pursuing self-actualization and people actually think. Using Herzberg’s model and other representations of motivation, we show that survival needs will attract people to forego their other needs, but also contribute to satisficing and freeriding. In the pursuit of passion, quality will shine through and with enough patience, the remainder of peoples’ needs will follow. This chapter includes an interview with movie producer and director L. E. Salas.