The theory of Markov decision processes (MDP) can be used to analyze a wide variety of stopping time problems in economics. In this paper, the nature of such problems is discussed and then the underlying theory is applied to the question of arranged marriages. We construct a stylized model of arranged marriages and, inter alia, it is shown that a decision maker's optimal policy depends only on the nature of the current marriage proposal, independent of whether (...) there is recall (storage) of previous marriage proposals. (shrink)

Individual differences in decisiveness: pre-decisional information search and decision strategy use We investigated whether individual differences in decisiveness are associated with a tendency to use different decision strategies during pre-decisional information search. To explore these potential links we administered the Need for Cognitive Closure questionnaire to 62 participants, followed by a probabilistic inference, multi-attribute choice task. Participants high in decisiveness dimension, compared to ‘low decisives’, spent less time and acquired less information prior to making decisions, especially in the (...) first trials of the choice task. ‘High decisives’ also had a greater tendency to use a simple Lexicographic heuristic than ‘low decisives’. The results support the view that high decisiveness is associated with greater tendency to simplify the decision process. (shrink)

At the basis of tireless efforts to explain the nature of law lies the question of how judges should decide cases. Therefrom arises a need for a theory that would clarify the role of the courts and, moreover, provide guidance to them on reaching judgments. The history of legal theory abounds with various attempts to offer a generally acceptable answer to the question raised. The fervor of debate and the perpetual dissatisfaction with offered solutions prompted the thought of untamable arbitrariness (...) of judges. In the contemporary debate the significance of argumentation is particularly emphasized as a link of the court procedure which provides reasonableness and therewith justification and persuasiveness of the decision. Before going into the matter, I will indicate in broad strokes which areas of legal theory do argumentation and interpretation belong to. The purpose of setting a conceptual framework is to prevent losing sight of the whole as well as to limit the scope of discourse to a certain section of legal issues. The second part deals with the concept of argumentation in general and some specific features of the argumentation in law. The third part examines the role of legal interpretation and draws a clear distinction between the interpretation as a process and the interpretation as a result. At the end of the discussion I shall put forward a thesis that the interpretation as a process is argumentation, while the interpretation as a result is an argument in the justification of judgment. nema. (shrink)

We give an example from the theory of Markov decision processes which shows that the “optimism in the face of uncertainty” heuristics may fail to make any progress. This is due to the impossibility to falsify a belief that a (transition) probability is larger than 0. Our example shows the utility of Popper’s demand of falsifiability of hypotheses in the area of artificial intelligence.

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision-theoretic approach and show how to formulate teaching as a partially observable Markov (...) class='Hi'>decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept-learning tasks, we show that this technique can accelerate learning relative to baseline performance. (shrink)

One most common problem in reinforcement learning systems (e.g. Q-learning) is to reduce the number of trials to converge to an optimal policy. As one of the solution to the problem, k-certainty exploration method was proposed. Miyazaki reported that this method could determine an optimal policy faster than Q-learning in Markov decision processes (MDPs). This method is very efficient learning method. But, we propose an improvement plan that makes this method more efficient. In k-certainty exploration method, in (...) case there is no k-uncertainty rule (rules which aren’t selected more than k times) in current state, an agent sometimes walks randomly until it finds a state where it can select a k-uncertainty rule. We think that this behavior is not efficient. To reduce this useless behavior, we propose combining k-certainty exploration method with Dynamic programming (DP). Miyazaki’s system uses DP after all rules are executed at least k times. But, our method uses k-certainty exploration method along with DP during learning. Our method, takes two pattern actions. In case an agent can select k-uncertainty rules, one of these rules is selected at random as k-certainty exploration method. In another case there is no k-uncertainty rule, behavior of agent is different from the behavior of k-certainty exploration method. In that case, our method uses DP to compute an optimal policy for moving from a current state to a state in which some k-uncertainty rules remain. The model for DP is constructed by using only known states. The outline will be described below. First, an agent makes a map constructed by using only known states. In the map, goals are states in which there are k-uncertainty rules and arbitrary state values are set in these states. Point to which attention should be paid is that the map is not given from outside. It is made from only experience. Next, each state values of states in which there are only k-certainty rules in the map are computed by DP (we used Policy Iteration). Finally, an agent continues to select greedy action until it arrives at a state in which it can select a k-uncertainty rule. By this improvement, we expect it can determine an optimal policy faster than k-certainty exploration method. And we have verified that our exploration method can determine an optimal policy faster than k-certainty exploration method by computer simulation. (shrink)

The ability to learn sequential contingencies of actions for predicting future outcomes is indispensable for flexible behavior in many daily decision-making contexts. It remains open whether such ability may be enhanced by transcranial direct current stimulation. The present study combined tDCS with functional near-infrared spectroscopy to investigate potential tDCS-induced effects on sequential decision-making and the neural mechanisms underlying such modulations. Offline tDCS and sham stimulation were applied over the left and right dorsolateral prefrontal cortex in young male adults (...) in a double-blind between-subject design using a three-state Markov decision task. The results showed an enhanced dlPFC hemodynamic response during the acquisition of sequential state transitions that is consistent with the findings from a previous functional magnetic resonance imaging study; a tDCS-induced increase of the hemodynamic response in the dlPFC, but without accompanying performance-enhancing effects at the behavioral level; and a greater tDCS-induced upregulation of hemodynamic responses in the delayed reward condition that seems to be associated with faster decision speed. Taken together, these findings provide empirical evidence for fNIRS as a suitable method for investigating hemodynamic correlates of sequential decision-making as well as functional brain correlates underlying tDCS-induced modulation. Future research with larger sample sizes for carrying out subgroup analysis is necessary in order to decipher interindividual differences in tDCS-induced effects on sequential decision-making process at the behavioral and brain levels. (shrink)

We consider a reinforcement learning setting where the learner is given a set of possible models containing the true model. While there are algorithms that are able to successfully learn optimal behavior in this setting, they do so without trying to identify the underlying true model. Indeed, we show that there are cases in which the attempt to find the true model is doomed to failure.

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. (...) We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration. (shrink)

Foraging is a central competence of all mobile organisms. Models and concepts from foraging theory have been applied widely throughout biology to the search for many kinds of external resources, including food, sexual encounters, minerals, water, and the like. In cognitive science and neuroscience, the tools of foraging theory are increasingly applied to a wide range of other types of search, including for abstract resources like information or for internal resources like memories, concepts, and strategies for problem solving. Despite its (...) importance in ecology and increasing relevance for the study of cognition, the concept of foraging is rarely analyzed. Here, I aim to rectify this situation. I outline three desiderata: first, an analysis should differentiate foraging from search and decision making more generally; second, an analysis should unify different types of foraging; and third, an analysis should help ground predictions. I present an analysis of foraging as the serial search for general resources in accept-or-reject, exclusive, persistent decision contexts. Not all search is serial and not all decision making is exclusive, differentiating foraging from search and decision making generally. With the aid of Markov decision processes and directed cyclical models, I show how the analysis implies a cyclical graph. This cyclical graph is embedded in the description of many types of foraging, unifying the different instances. Finally, I argue that the cyclical graph is also embedded in representations of novel task contexts that have not previously been viewed as foraging. I illustrate this novel application of the concept of foraging by arguing that reasoning is a type of foraging. (shrink)

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our (...) approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game. (shrink)

Reinforcement Learning (RL) handles policy search problems: searching a mapping from state space to action space. However RL is based on gradient methods and as such, cannot deal with problems with multimodal landscape. In contrast, though Genetic Algorithm (GA) is promising to deal with them, it seems to be unsuitable for policy search problems from the viewpoint of the cost of evaluation. Minimal Generation Gap (MGG), used as a generation-alternation model in GA, generates many offspring from two or more parents (...) selected from a population. Therefore, evaluating policies of generated offspring requires much trial and error (i.e. interaction between an agent and an environment). In this paper, we incorporate importance sampling into the framework of MGG in order to reduce the cost of evaluation on policy search. The proposed techniques are applied to Markov Decision Process (MDP) with multimodal landscape. The experimental results show that these techniques can reduce the number of interaction between an agent and an environment, and also mean that MGG and importance sampling are good for each other. (shrink)

Profit Sharing is one of the reinforcement learning methods. An agent, as a learner, selects an action with a state-action value and receives rewards when it reaches a goal state. Then it distributes receiving rewards to state-action values. This paper discusses how to set the initial value of a state-action value. A distribution function ƒ( x ) is called as the reinforcement function. On Profit Sharing, an agent learns a policy by distributing rewards with the reinforcement function. On Markov (...) Decision Processes (MDPs), the reinforcement function ƒ( x ) = 1/ L x is useful, and on Partially Observable Markov Decision Processes (POMDPs), ƒ( x ) = 1/ L w is useful, where L is the sufficient number of rules at each state, and W is the length of an episode. If episodes are always long, the value of the reinforcement function is little. So the differences of rule values become little, and the agent learns little by using the roulette selection as an action selection. This problem is called as Learning Speed Problem. If the value of the reinforcement function for an action is very higher than its state-action value, an agent will not select other action. There is a problem when its action is not a optimal action. This problem is called as Past Experiences Problem. This paper shows that both Learning Speed Problem and Past Experiences Problem are caused by the bad setting between the initial values of a state-action values and the function values of a reinforcement function. We propose how to set the initial values of a state-action values at each state. The experiment shows that an agent can learn correctly even if the length of episode is large. And shows the effectiveness on both MDPs and POMDPs. Our proposed method focuses on the initialization of state-action values and does not limit reinforcement functions. So it can apply to any reinforcement function. (shrink)

As autonomous mobile robots (AMRs) begin living in the home, performing service tasks and assisting with daily activities, their actions will have profound ethical implications. Consequently, AMRs need to be outfitted with the ability to act morally with regard to human life and safety. Yet, in the area of robotics where morality is a relevant field of endeavor (i.e. human-robot interaction) the sub-discipline of morality does not exist. In response, the Utilibot project seeks to provide a point of initiation for (...) the implementation of ethics in an AMR. The Utilibot is a decision-theoretic AMR guided by the utilitarian notion of the maximization of human well-being. The core ethical decision-making capacity of the Utilibot consists of two dynamic Bayesian networks that model human and environmental health, a dynamic decision network that accounts for decisions and utilities, and a Markov decision process (MDP) that decomposes the planning problem to solve for the optimal course of action to maximize human safety and well-being. (shrink)

Reinforcement Learning is a kind of machine learning. We know Profit Sharing, the Rational Policy Making algorithm, the Penalty Avoiding Rational Policy Making algorithm and PS-r* to guarantee the rationality in a typical class of the Partially Observable Markov Decision Processes. However they cannot treat continuous state spaces. In this paper, we present a solution to adapt them in continuous state spaces. We give RPM a mechanism to treat continuous state spaces in the environment that has the (...) same type of a reward. We show the effectiveness of the proposed method in numerical examples. (shrink)

In a previous paper, we proposed a solution to navigation of a mobile robot. In our approach, we formulated the following two problems at each time step as discrete optimization problems: 1) estimation of position and direction of a robot, and 2)action decision. While the results of our simulation showed the effectiveness of our approach, the values of weights in the objective functions were given by a heuristic method. This paper presents a theoretical method using reinforcement learning for adjusting (...) the weight parameters in the objective function that includes pieces of heuristic knowledge on the action decision. In our reinforcement learning, the expectation of a reward given to a robot’s trajectory is defined as the value function to maximize. The robot’s trajectories are generated stochastically because we used a probabilistic policy for determining actions of a robot to search for the global optimal trajectory. However, this decision process is not a Markov decision process because the objective function includes an action at the previous time. Thus, Q-learning, which is a conventional method of reinforcement learning, cannot be applied to this problem. In this paper, we applied Williams’s episodic REINFORCE approach to the action decision and derived a learning rule for the weight parameters of the objective function. Moreover, we applied the stochastic hill-climbing method to maximizing the value function to reduce computation time. The learning rule was verified by our experiment. (shrink)

Reinforcement learning is a kind of machine learning. It aims to adapt an agent to a given environment with a clue to rewards. In general, the purpose of reinforcement learning system is to acquire an optimum policy that can maximize expected reward per an action. However, it is not always important for any environment. Especially, if we apply reinforcement learning system to engineering, environments, we expect the agent to avoid all penalties. In Markov Decision Processes, a pair (...) of a sensory input and an action is called rule. We call a rule penalty if and only if it has a penalty or it can transit to a penalty state where it does not contribute to get any reward. After suppressing all penalty rules, we aim to make a rational policy whose expected reward per an action is larger than zero. In this paper, we propose a suppressing penalty algorithm that can suppress any penalty and get a reward constantly. By applying the algorithm to the tick-tack-toe, its effectiveness is shown. (shrink)

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov (...) class='Hi'>decision process. Then, the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step‐by‐step solutions, we (...) propose a Bayesian inverse planning model for equation solving that computes an assessment of a learner's skills based on her pattern of errors in individual steps and her choices about what sequence of problem‐solving steps to take. Bayesian inverse planning builds on existing machine learning tools to create a generative model relating (mis)‐understandings to equation solving choices. Two behavioral experiments demonstrate that the model can interpret people's equation solving and that its assessments are consistent with those of experienced teachers. A third experiment uses this model to tailor guidance for learners based on individual differences in misunderstandings, closing the loop between assessing understanding, and using that assessment within an educational technology. Finally, because the bottleneck in applying inverse planning to a new domain is in creating the model of possible student misunderstandings, we show how to combine inverse planning with an existing production rule model to make inferences about student misunderstandings of fraction arithmetic. (shrink)

Seventy-three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: a non-monotonic dependence of performance as a function of the distance between the beginning and the end-states of the problem, very high levels of performance when the first move was correct, and states in which accuracy of the first move was significantly below chance. These features are consistent with a non-Markov planning agent, with an (...) inherently inertial decision process, and that uses heuristics and partial problem knowledge to plan its actions. We applied a statistical framework to fit and test the quality of a proposed planning model. Our framework allows us to parse out independent contributions to problem-solving based on the construction of the value function and on general mechanisms of the search process in the tree of solutions. We show that the latter are correlated with children's performance on an independent measure of planning, while the former is highly domain specific. (shrink)

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with (...) tools internal to PP theory: Miller and Clark’s (2018) embodied inference; Seth’s (2013) interoceptive inference; and Joffily and Coricelli’s (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain ‘s cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. (shrink)

Unchosen transformative experiences—transformative experiences that are imposed upon an agent by external circumstances—present a fundamental problem for agency: how does one act intentionally in circumstances that transform oneself as an agent, and that disrupt one’s core projects, cares, or goals? Drawing from William James’s analysis of conversion and Matthew Ratcliffe’s account of grief, I give a phenomenological analysis of transformative experiences as involving the restructuring of systems of practical meaning. On this analysis, an agent’s experience of the world is structured (...) by practically significant possibilities that form organized systems on the basis of the agent’s projects and relationships. Transformative experiences involve shifts to systems of possibility, that is, changes to habitual meanings and to how an agent’s projects are situated in relation to one another. I employ the enactivist notion of sense-making to analyze how an agent rebuilds the meaning structures disrupted by a transformative experience. In an unchosen transformative experience, an agent adjusts to a significant disruption through a process of sense-making in precarious conditions. By establishing new patterns of bodily and social interaction with the world, one alters the practical meanings of one’s surroundings, and thereby reconstitutes oneself as an intentional agent. (shrink)

Unchosen transformative experiences—transformative experiences that are imposed upon an agent by external circumstances—present a fundamental problem for agency: how does one act intentionally in circumstances that transform oneself as an agent, and that disrupt one’s core projects, cares, or goals? Drawing from William James’s analysis of conversion and Matthew Ratcliffe’s account of grief, I give a phenomenological analysis of transformative experiences as involving the restructuring of systems of practical meaning. On this analysis, an agent’s experience of the world is structured (...) by practically significant possibilities that form organized systems on the basis of the agent’s projects and relationships. Transformative experiences involve shifts to systems of possibility, that is, changes to habitual meanings and to how an agent’s projects are situated in relation to one another. I employ the enactivist notion of sense-making to analyze how an agent rebuilds the meaning structures disrupted by a transformative experience. In an unchosen transformative experience, an agent adjusts to a significant disruption through a process of sense-making in precarious conditions. By establishing new patterns of bodily and social interaction with the world, one alters the practical meanings of one’s surroundings, and thereby reconstitutes oneself as an intentional agent. (shrink)

Emotionally arousing events reach awareness more easily and evoke greater visual cortex activation than more mundane events. Recent studies have shown that they are also perceived more vividly and that emotionally enhanced perceptual vividness predicts memory vividness. We propose that affect-biased attention (ABA) – selective attention to emotionally salient events – is an endogenous attentional system tuned by an individual's history of reward and punishment. We present the Biased Attention via Norepinephrine (BANE) model, which unifies genetic, neuromodulatory, neural and behavioural (...) evidence to account for ABA. We review evidence supporting BANE's proposal that a key mechanism of ABA is locus coeruleus–norepinephrine (LC–NE) activity, which interacts with activity in hubs of affective salience networks to modulate visual cortex activation and heighten the subjective vividness of emotionally salient stimuli. We further review literature on biased competition and look at initial evidence for its potential as a neural mechanism behind ABA. We also review evidence supporting the role of the LC–NE system as a driving force of ABA. Finally, we review individual differences in ABA and memory including differences in sensitivity to stimulus category and valence. We focus on differences arising from a variant of the ADRA2b gene, which codes for the alpha2b adrenoreceptor as a way of investigating influences of NE availability on ABA in humans. (shrink)

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has (...) been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. (shrink)

Regional planning has been regarded as a design activity. Usually planners focus on physical design rather than on societal issues. Nowadays, mass communication, environmental issues and social awareness lead to often complex and conflicting needs and interests of the public in regional planning. This paper focuses on the regional planning as a group problem solving process from the view of information processing. It offers an analysis of the causes of conflicts in the group decision process, and defines the characteristics (...) of group decision process support systems. (shrink)

Underscoring the economic significance of the Knightian distinction between risk and uncertainty, Don Katzner forcefully challenges the continued dominance of the expected utility model based on subjective probability in macroeconomic analysis and offers in its place a simple yet elegant model of decision making inspired by the pioneering work of G.L.S. Shackle. In doing so, Katzner lends support to a research program to identify a more coherent and empirically grounded theory of decision making under uncertainty. Our paper makes (...) three contributions to this program. First, we argue that the appropriate choice of a model of individual behavior under uncertainty cannot be adjudicated merely on logical, a priori grounds, but must ultimately be based on consistency with observed behavior. There are many internally consistent models and their external conditions of validity hardly ever rely on inconceivable scenarios. Second, while Katzner’s model certainly provides a plausible foundation for understanding choice under uncertainty, we suggest that it is somewhat underdetermined. It requires further theoretical articulation to yield testable predictions which differentiate it from competing approaches. Third, we discuss recent experimental findings that may need to be addressed by any satisfactory theoretical account of decision making under uncertainty. (shrink)

In an ever more transparent, digitalized, and connected environment, customers are increasingly pressuring brands to embrace genuine corporate social responsibility practices and co-creation activities. While both CSR and co-creation are social and collaborative processes, there is still little research examining whether CSR can boost co-creation. In addition, while previous research has mainly related co-creation to emotional outcomes, limited empirical research has related it to rational and behavioral outcomes. To address these shortcomings in the literature, this paper examines the influence (...) of CSR on customer loyalty, considering the mediating roles of co-creation and customer trust. It also investigates the influence of co-creation on customer trust. The data were collected in Spain in late 2017 using an online survey, and the sample contained 1101 customers of health insurance services brands. Structural equation modeling was used to test the hypothesized relationships simultaneously. The results show that CSR influences customer loyalty both directly and indirectly through co-creation and customer trust. However, the indirect impact is the stronger of the two, implying that embracing co-creation activities and developing customer trust can make it easier for CSR practices to enhance customer loyalty. In addition, co-creation has a direct effect on customer trust. (shrink)

The influence of stakeholders, organisational commitment, personal values, goals of the organisation and socio-demographic characteristics of individuals on the ethical dimension of behavioural intentions of employees in various organisations are investigated. The research results show that employees working for the public sector or in educational institutions take more ethical aspects into account than employees working in the "private" sector. The influence of stakeholders and organisational commitment do not significantly affect the ethical behaviour of employees, and only some personal values and (...) goals of the organisation have a significant influence on ethical behaviour. The most significant explanatory factor of ethical decision making seems to be what may be called "stage in the career of the employee": "ethical" employees can be described as young, with a relatively low income, limited work experience and a low level of responsibility in the company. (shrink)

The comparison between biological and social macroevolution is a very important (though insufficiently studied) subject whose analysis renders new significant possibilities to comprehend the processes, trends, mechanisms, and peculiarities of each of the two types of macroevolution. Of course, there are a few rather important (and very understandable) differences between them; however, it appears possible to identify a number of fundamental similarities. One may single out at least three fundamental sets of factors determining those similarities. First of all, those (...) similarities stem from the fact that in both cases we are dealing with very complex non-equilibrium (but rather stable) systems whose principles of functioning and evolution are described by the General Systems' Theory, as well as by a number of cybernetic principles and laws. -/- Secondly, in both cases we do not deal with isolated systems; in both cases we deal with a complex interaction between systems of organic systems and external environment, whereas the reaction of systems to external challenges can be described in terms of certain general principles (that, however, express themselves rather differently within the biological reality, on the one hand, and within the social reality, on the other). -/- Thirdly, it is necessary to mention a direct ‘genetic’ link between the two types of macroevolution and their mutual influence. -/- It is important to emphasize that the very similarity of the principles and regularities of the two types of macroevolution does not imply their identity. Rather significant similarities are frequently accompanied by enormous differences. For example, genomes of the chimpanzees and the humans are very similar – with differences constituting just a few per cent; however, there are enormous differences with respect to intellectual and social differences of the chimpanzees and the humans hidden behind the apparently ‘insignificant’ difference between the two genomes. Thus, in certain respects it appears reasonable to consider the biological and social macroevolution as a single macroevolutionary process. This implies the necessity to comprehend the general laws and regularities that describe this process, though their manifestations may display significant variations depending on properties of a concrete evolving entity (biological, or social one). An important notion that may contribute to the improvement of the operationalization level as regards the comparison between the two types of macroevolution is the one that we suggested some time ago – the social aromorphosis (that was developed as a counterpart to the notion of biological aromorphosis well established within Russian evolutionary biology). We regard social aromorphosis as a rare qualitative macrochange that increases in a very significant way complexity, adaptability, and mutual influence of the social systems, that opens new possibilities for social macrodevelopment. In our paper we discuss a number of regularities that describe biological and social macroevolution and that employ the notions of social and biological aromorphosis such as ones of the module evolution (or the evolutionary ‘block assemblage’), ‘payment for arogenic progress’ etc. (shrink)

In this chapter, it is demonstrated that the concepts of leadership and organization are closely linked. A leader should initially get to know the organizational culture as well as possible. Such a culture can for example be authoritarian and conformist or innovative and progressive in nature. The assumption is that leaders are influenced by their own culture. Strategic decisions are characterized by the fact that they are new, complex and open in nature, and being able to develop a strategy is (...) one of the most difficult tasks for a leader. Traditionally, it is primarily the top leadership in an organization that works with strategic decisions, and thus it is common that strategic issues are handled by top leadership teams. This is related to the globalization of business and to the fact that the pace of work has increased significantly. In order to exercise leadership, a leader must have access to power. A power base can be created through networking as well as by using different political tactics. However, it is important to use political tactics in order to promote the organization's interests. When a leader has built up a power base, it is essential that power is used properly. The decisions that leaders make must be ethically correct and not violate universal human values. For instance, they should not lead to negative consequences for others within or outside the organization. Evidence suggests that most leaders have the potential to develop as ethical decision makers. -/- . (shrink)

This paper argues that grieving a profound loss is a transformative experience, specifically an unchosen transformative experience, understood as an event‐based transformation not chosen by the agent. Grief transforms the self (i) cognitively, by forcing the agent to alter a large set of beliefs and desires, (ii) phenomenologically, by altering their experience in a diffuse or global manner, (iii) normatively, by requiring the agent to revise their practical identity, and (iv) existentially, by confronting the agent with a structuring condition of (...) their life. Grief is a disruption to one's identity that an agent addresses by making sense of the world after the loss, remaking the practical significance of various situations in their lives through their activity. Transformative grief is both an “activity” and a “revelation” (Callard, 2020): Some parts of the grieving process are active (the agent must actively work to become a new kind of person), while others are irreducibly passive (the agent passively undergoes them). (shrink)

In this paper, we present a method for introducing law students to the writing of legal documents. The method uses a machine-readable representation of the legal knowledge to support document assembly and to help the students to understand how the assembly is performed. The knowledge base consists of enacted legislation, document templates, and assembly instructions. We propose a system called LEDAS (LEgal Document Assembly System) for the interactive assembly of legal documents. It guides users through the assembly process and provides (...) explanations of the interconnection between input data and claims stated in the document. The system acts as a platform for practicing drafting skills and has great potential as an education tool. It allows teachers to configure the system for the assembly of some particular type of legal document and then enables students to draft the documents by investigating which information is relevant for these documents and how the input data shape the final document. The generated legal document is complemented by a graphical representation of legal arguments expressed in the document. The system is based on existing legal standards to facilitate its introduction in the legal domain. Applicability of the system in the education of future lawyers is positively evaluated by the group of law students and their TA. (shrink)