Social learning with heterogeneous agents and sequential decision making

In this paper, we consider the problem of social learning in a network of agents where the agents make decisions sequentially by choosing one of two hypotheses on the state of nature. Each agent observes a signal generated according to one of the hypotheses and knows the decisions of all the previous agents in the network. The network contains two types of agents: rational and irrational. A rational agent makes a decision by not only using its private observation but also the decisions of each of the agents which already made decisions. To that end, the agent employs Bayesian theory. An irrational agent makes a decision by ignoring the available information and by randomly choosing the hypothesis. We analyze the asymptotic performance of a system with rational and irrational agents where we study rational agents that use either a deterministic or random decision making policies. We propose a specific random decision making policy that is based on the social belief and the private signals of the agents. We prove that under mild conditions the expected social belief in the true state of nature tends to one if the rational agents use the proposed random policy. In a network with rational agents that use deterministic policy, the conditions for convergence are stricter. We provide simulation results on the studied systems and compare their performance.     

Proving classical theorems of social choice theory in modal logic

A number of seminal results in the field of social choice theory demonstrate the difficulties of aggregating the preferences of several individual agents for the purpose of making a decision together. We show how to formalise three of the most important impossibility results of this kind—Arrow’s Theorem, Sen’s Theorem, and the Muller–Satterthwaite Theorem—by using a modal logic of social choice functions. We also provide syntactic proofs of these theorems in the same logic. While prior work has been successful in applying tools from logic and automated reasoning to social choice theory, this is the first human-readable formalisation of the Arrovian framework allowing for a direct derivation of the main impossibility theorems of social choice theory. This is useful for gaining a deeper understanding of the foundations of collective decision making, both in human society and in groups of autonomous software agents.     

The impact of fuzziness in social choice paradoxes

Since Arrow’s main theorem showed the impossibility of a rational procedure in group decision making, many variations in restrictions and objectives have been introduced in order to find out the limits of such a negative result. But so far all those results are often presented as a proof of the great expected difficulties we always shall find pursuing a joint group decision from different individual opinions, if we pursue rational and ethical procedures. In this paper we shall review some of the alternative approaches fuzzy sets theory allows, showing among other things that the main assumption of Arrow’s model, not being made explicit in his famous theorem, was its underlying binary logic (a crisp definition is implied in preferences, consistency, liberty, equality, consensus and every concept or piece of information). Moreover, we shall also point out that focusing the problem on the choice issue can be also misleading, at least when dealing with human behaviour.     

The decision making basis for information systems: the contribution of Vickers' concept of appreciation to a soft systems perspective

This paper considers the models of decision making which are used within the field of information systems. It argues that although decision making is of great relevance to the study of information systems insufficient attention has been given to the models by which IS professionals understand the process. Hard systems thinking has made a great contribution to IS in providing rational models of decision making, the work of H. A. Simon having been particularly influential; but the simplistic way in which such work is presented in IS texts ignores the role of social and political factors in decision making and imposes constraints in the complex social situations in which real world decision making occurs. An important contribution which the soft strand of systems thinking may make to IS is to provide a richer understanding of the nature of decision making through Vickers' concept of appreciation. The resultant ‘appreciative model’ of decision making emphasises the importance of the decision makers' perception of the organization and its situation in the interpretation of data, provides the basis for making a clear distinction between ‘data’ and ‘information’, and has implications for the ways in which computerized information systems are developed.     

多人多级递阶决策的几个问题——鼓励性对策及模型简化

本文讨论了按鼓励性对策法则进行的二人、二级决策问题.利用可诱导区的概念对这一 问题解的结构给予了完整的描述并总结出将其化为单人决策问题的等价原理.这一原理可以 推广到一般情况,并可用于简化一类多人、多级决策问题.一些特殊情形也可以利用这一途径 求解.文中给出了数值例子.     

Establishing dominance and potential optimality in multi-criteria analysis with imprecise weight and value

The model presented in this paper does not require exact estimations of decision parameters such as attribute weights and values that may often be considerable cognitive burden of human decision makers. Information on the decision parameters is only assumed to be in the form of arbitrary linear inequalities which form constraints in the model. We consider two criteria, dominance and potential optimality, to check whether or not each alternative is outperform for a fixed feasible region denoted by the constraints. In particular, we develop a method to identify potential optimality of alternatives when all (or subsets) of the attribute values as well as weights are imprecisely know. This formulation becomes a nonlinear programming problem hard to be solved generally so that we provide in this paper how this problem is transformed into a linear programming equivalent.Scope and purposeMost managerial decisions involve choosing an optimal alternative from a number of available alternatives. Researchers have proposed a lot of methods to assist decision makers in choice making with a set of, usually conflicting, criteria or attributes. Many of these approaches require exact (or precise) information about either or both attribute values and/or trade-off weights. In some practice, however, it is not easy for decision makers to provide such exact data because, for example, intangible attributes to reflect social and environmental impacts may be included. To cope with such problem, a mathematical programming model-based approach to multi-criteria decision analysis is presented in this paper when both attribute weights and marginal values are imprecisely identified. A weighted additive rule is used to evaluate the performance of alternatives. We then show how to obtain non-dominated and potentially optimal alternatives in order to support choice making.     

Judgement aggregation and distributed thinking

In recent years, judgement aggregation has emerged as an important area of social choice theory. Judgement aggregation is concerned with aggregating sets of individual judgements over logically connected propositions into a set of collective judgements. It has been shown that even seemingly weak conditions on the aggregation function make it impossible to find functions that produce rational collective judgements from all possible rational individual judgements. This implies that the step from individual judgements to collective judgements requires trade-offs between different desiderata, such as universal domain, rationality, epistemological quality, and unbiasedness. The typical application for judgement aggregation is the problem of group decision making. Juries and expert committees are the stock examples. However, the relevance of judgement aggregation goes beyond these cases. In this survey, I review some core results in the field of judgement aggregation and social epistemology and discuss their implications for the analysis of distributed thinking.     

A model of design decision making based on empirical results of interviews with software designers

Despite the impact of design decisions on software design, we have little understanding about how design decisions are made. This hinders our ability to provide design metrics, processes and training that support inherent design work. By interviewing 25 software designers and using content analysis and explanation building as our analysis technique, we provide qualitative and quantitative results that highlight aspects of rational and naturalistic decision making in software design. Our qualitative multi-case study results in a model of design decision making to answer the question: how do software designers make design decisions? We find the structure of the design problem determines the aspects of rational and naturalistic decision making used. The more structured the design decision, the less a designer considers options.     

The Role of Intuition and Deliberation for Exploration and Exploitation Success

Advances in social and cognitive psychology and related fields have drawn attention to the role of intuition in organizational decision making. In this study we link intuitive and deliberate decision‐making styles to the success of exploration and exploitation activities, which are understood as two qualitatively opposing strategies that organizations can adopt. We provide empirical evidence that the two opposing strategies are linked to two opposing styles of decision making – intuitive and deliberate. In doing so, we draw on data which we received from 140 entrepreneurs and managing partners of Austrian companies and show that exploration is strongly related to intuitive decision making whereas exploitation draws on both intuitive and deliberate decision making. Based on our findings, we stress the complementarity of the two decision‐making styles, and point out that particularly in the light of the fast‐changing premises in which organizations have to manoeuvre today, decision makers are well advised to use both decision‐making styles to their best benefit.     

Case‐based approach for supporting strategy decision making

The practice of strategy decision making proves that when the management board is strongly limited in its capacity to take rational actions, specifically in the context of great decision complexity and uncertainty, it is considered good practice to refer to experience through reasoning by analogy. In this paper, we would like to concentrate on supporting strategic decisions in small and medium enterprises (SMEs). The complexity of analogy‐based reasoning has its roots in an attempt to solve new problems based on past cases from a different domain, while we will focus on case‐based approach for a single domain. Additionally, we have chosen case‐based reasoning as a suitable decision‐making paradigm because it is corresponds to managers’ decision‐making behaviour. We present the STRATEGOS case‐based reasoning system for supporting strategic decision making by SMEs management boards and then the system evaluation by the dozens of chief executive officers (CEOs) from SMEs is presented. The results of the survey are promising and show the remarkable correspondence of the proposed solution with expectations and strategic behaviour of CEOs.     

Artificial Agent Action in Markets

We summarise our experiences of a number of demonstrators and simulation experiments designed to test the feasibility of using artificial decision making agents in real-time domains, and comment on the significance of our results to autonomous artificial agent action patterns in markets. Our main hypothesis is that the use of norms can extend the capability of artificial decision makers beyond what is obtained from implementing individual utility maximizers in keeping with rational choice theory.     

The actor-critic learning is behind the matching law: matching versus optimal behaviors

The ability to make a correct choice of behavior from various options is crucial for animals' survival. The neural basis for the choice of behavior has been attracting growing attention in research on biological and artificial neural systems. Alternative choice tasks with variable ratio (VR) and variable interval (VI) schedules of reinforcement have often been employed in studying decision making by animals and humans. In the VR schedule task, alternative choices are reinforced with different probabilities, and subjects learn to select the behavioral response rewarded more frequently. In the VI schedule task, alternative choices are reinforced at different average intervals independent of the choice frequencies, and the choice behavior follows the so-called matching law. The two policies appear robustly in subjects' choice of behavior, but the underlying neural mechanisms remain unknown. Here, we show that these seemingly different policies can appear from a common computational algorithm known as actor-critic learning. We present experimentally testable variations of the VI schedule in which the matching behavior gives only a suboptimal solution to decision making and show that the actor-critic system exhibits the matching behavior in the steady state of the learning even when the matching behavior is suboptimal. However, it is found that the matching behavior can earn approximately the same reward as the optimal one in many practical situations.     

Decision making on the sole basis of statistical likelihood

This paper presents a new axiomatic decision theory for choice under uncertainty. Unlike Bayesian decision theory where uncertainty is represented by a probability function, in our theory, uncertainty is given in the form of a likelihood function extracted from statistical evidence. The likelihood principle in statistics stipulates that likelihood functions encode all relevant information obtainable from experimental data. In particular, we do not assume any knowledge of prior probabilities. Consequently, a Bayesian conversion of likelihoods to posterior probabilities is not possible in our setting. We make an assumption that defines the likelihood of a set of hypotheses as the maximum likelihood over the elements of the set. We justify an axiomatic system similar to that used by von Neumann and Morgenstern for choice under risk. Our main result is a representation theorem using the new concept of binary utility. We also discuss how ambiguity attitudes are handled. Applied to the statistical inference problem, our theory suggests a novel solution. The results in this paper could be useful for probabilistic model selection.     

The parameterization reduction of soft sets and its applications

In this paper, we focus our discussion on the parameterization reduction of soft sets and its applications. First we point out that the results of soft set reductions offered in [1] are incorrect. We also observe that the algorithms used to first compute the reduct-soft-set and then to compute the choice value to select the optimal objects for the decision problems in [1] are not reasonable and we illustrate this with an example. Finally, we propose a reasonable definition of parameterization reduction of soft sets and compare it with the concept of attributes reduction in rough sets theory. By using this new definition of parameterization reduction, we improve the application of a soft set in a decision making problem found in [1].     

复杂系统的质量生存可达性决策

为进一步刻画复杂系统的有限理性决策，在复杂系统生存可达性决策的基础上，引入生存质量函数，提出质量生存可达性决策的概念，并给出 了质量生存可达性决策存在的充要条件。讨论了复杂系统质量生存可达性决策的一般特点和决策方法。     

Preferences in artificial intelligence

The paper presents a focused survey about the presence and the use of the concept of “preferences” in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic.     

Social fMRI: Investigating and shaping social mechanisms in the real world

We introduce the Friends and Family study, a longitudinal living laboratory in a residential community. In this study, we employ a ubiquitous computing approach, Social Functional Mechanism-design and Relationship Imaging, or Social fMRI, that combines extremely rich data collection with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, deployed in the wild for over 15 months. Finally, we present three investigations performed during the study, looking into the connection between individuals’ social behavior and their financial status, network effects in decision making, and a novel intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for choice, motivation, and adherence, and enable quantifying the contribution of different incentive mechanisms.     

Minimizing deviations of input and output weights from their means in data envelopment analysis

In this paper, we propose a model that minimizes deviations of input and output weights from their means for efficient decision-making units in data envelopment analysis. The mean of an input or output weight is defined as the average of the maximum and the minimum attainable values of the weight when the efficient decision making unit under evaluation remains efficient. Alternate optimal weights usually exist in the linear programming solutions of efficient decision-making units and the optimal weights obtained from most of the linear programming software are somewhat arbitrary. Our proposed model can yield more rational weights without a priori information about the weights. Input and output weights can be used to compute cross-efficiencies of decision-making units in peer evaluations or group decision-making units, which have similar production processes via cluster analysis. If decision makers want to avoid using weights with extreme or zero values to access performance of decision-making units, then choosing weights that are close to their means, may be a rational choice.