Decision Maker Chooses Research Articles

A condition-based maintenance policy for a two-component balanced system with dependent degradation processes

Preventive maintenance of balanced systems has garnered increasing research attention due to its significance in production and servicing processes. This paper investigates the optimization problem of a condition-based maintenance policy for a two-component balanced system with dependent degradation processes. The system’s degradation follows a bivariate gamma process. Once the degradation level of one component exceeds a critical value, the system fails and triggers an immediate corrective replacement. Moreover, if the degradation difference between two components exceeds a threshold, the system is in an unbalanced state, which incurs a penalty cost. Periodic inspections are implemented to reveal the degradation of the system. At each inspection epoch, the decision-maker chooses an action among do-nothing, repair, and preventive replacement. Different from previous research, a repair action does not reduce the degradation levels of either component but increases the degradation level of the lower one to rebalance the system. The objective is to determine the optimal condition-based maintenance policy that minimizes the long-run average cost rate. The optimization problem is analyzed in a semi-Markov decision process framework. A policy-iteration dynamic programming algorithm is developed to derive the optimal policy. A numerical example is presented to illustrate the effectiveness of the proposed approach.

A multi-objective approach to identify parameters of compartmental epidemiological models—Application to Ebola Virus Disease epidemics

In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (Parallel Weighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model.

Risk-taking for others: An experiment on the role of moral discussion

We study the effect of moral discussion when risk-taking choices entail a negative externality on others. In our experiment, the decision-maker chooses between two risky gambles, one of which entails a better outcome for himself but higher risk for the receiver. In the Moral Discussion treatments – before making a choice – decision-makers discuss the consequences of their choice within a group of peers. We also implement a Reflection treatment, where participants have to think before making their choice, and a baseline with an immediate decision. Our results show that, after a moral discussion, decision-makers choose more often the less risky gamble compared to the Reflection. Moreover, this effect does not depend on the mode of interactions among participants. Through a mediation analysis, we also show that this effect mainly unfolds through a significant modification of the beliefs about the behaviour of their peers.

Attention and choices with multiple states and actions: A laboratory experiment

We study how a rationally inattentive decision maker chooses state-contingent actions under uncertainty in complex environments. We explore a series of decision problems by varying the number of states as well as incentive structures. We fully characterize the theoretical solutions and compare them to choices made by subjects facing those problems in a controlled laboratory experiment.Observed behavior is broadly consistent with the theoretical model, with subjects responding to changes in complexity and incentives by varying their level of attention. Nevertheless, some interesting differences emerge from the experimental data. In particular, we find only mixed support for the invariance under compression property, that perceptual factors may be required to explain some aspects of subject behavior, and that complexity can affect the ratio of expected utility to information gains.

Predicting Decisions in Language Based Persuasion Games

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions.
 For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert’s payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews’ text, and observe substantial differences which can be explained through an equilibrium analysis of the game.
 We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup.

Constrained Online Convex Optimization With Feedback Delays

In this article, we study constrained online convex optimization (OCO) in the presence of feedback delays, where a decision maker chooses sequential actions without knowing the loss functions and constraint functions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . The loss/constraint functions vary with time and their feedback information is revealed to the decision maker with delays, which arise in many applications. We first consider the scenario of delayed function feedback, in which the complete information of the loss/constraint functions is revealed to the decision maker with delays. We develop a modified online saddle point algorithm suitable for constrained OCO with feedback delays. Sublinear regret and sublinear constraint violation bounds are established for the algorithm in terms of the delays. In practice, the complete information (functional forms) of the loss/constraint functions may not be revealed to the decision maker. Thus, we further examine the scenario of delayed bandit feedback, where only the values of the loss/constraint functions at two random points close to the chosen action are revealed to the decision maker with delays. A delayed version of the bandit online saddle point algorithm is proposed by utilizing stochastic gradient estimates of the loss/constraint functions based on delayed bandit feedback. We also establish sublinear regret and sublinear constraint violation bounds for this bandit optimization algorithm in terms of the delays. Finally, numerical results for online quadratically constrained quadratic programs are presented to corroborate the efficacy of the proposed algorithms.

Comparative Analysis of the Normalization Techniques in the Context of MCDM Problems

NormNormalization is an essential step in data analysis and for MCDM methods. This study aims to outline the positive and negative features of the normalization techniques that can be used in MCDM problems. In order to compare the different normalization techniques, fourteen sets representing different scenarios of decision problems were used. According to the results, if the decision-maker chooses to take the alternative with the highest value in the criteria and avoid the one with the lowest value, or vice versa, optimization-based normalization techniques should be preferred, whereas the reference-based normalization techniques are considered appropriate for situations where there are ideal values determined by the decision-maker for each criterion. However, if the decision-maker believes that the values in the criteria do not represent the monotonous increasing or decreasing benefit/cost, then non-linear normalization techniques should be used. Also, in the event of a change in the conditions mentioned above, the decision maker may opt for mixed normalization techniques. However, some data structures, such as the presence of zero, and negative values in the decision matrix, can prevent the use of some normalization techniques. The choice of the normalization technique may also be affected by the problem of rank reversal, the range of normalized values, obtaining the same optimization aspect for all criteria, and the validity of results.

Near-Optimal Experimental Design for Networks: Independent Block Randomization

Motivated by the prevalence of experimentation in online platforms and social networks, we consider the problem of designing randomized experiments to assess the effectiveness of a new market intervention for a network of users. An experiment assigns each user to either the treatment or the control group. The outcome of each user depends on her assignment as well as the assignments of her neighbors. Given the experiment, the unbiased Horvitz-Thompson estimator is used to estimate the total market effect of the treatment. The decision maker chooses randomized assignments of users to treatment and control, in order to minimize the worst-case variance of this estimator. We focus on networks that can be partitioned into communities, where the users in the same community are densely connected, and users from different communities are only loosely connected. In such settings, it is almost without loss to assign all users in the same community to the same variant (treatment or control). The problem of designing the optimal randomized assignments of communities can be formulated as a linear program with exponential number of decision variables and constraints in the number of communities -- and hence is in general computationally intractable. We develop a family of practical experiments that we refer to as \emph{independent block randomization (IBR)} experiments. Such an experiment partitions communities into blocks so that each block contains communities of similar sizes. It then treats half of the communities in each block (chosen uniformly at random) and does so independently across blocks. The optimal community partition can be obtained in a tractable way using dynamic programming. We show that these policies are asymptotically optimal when the number of communities grows large and no community size dominates the rest. In the special case where community sizes take values in a finite set and the number of communities of each size is a fixed proportion of the total number of communities, the loss is only a constant that is independent of the number of communities. Beyond the asymptotic regime, we show that the IBR experiment is a 7/3-approximation for any problem instance. We also examine the performance of the IBR experiments on data-driven numerical examples, including an example based on a Facebook subnetwork.

Dual choice axiom and probabilistic choice

A decision maker chooses in a probabilistic manner if she does not necessarily prefer the same choice alternative when repeatedly presented with the same choice set. Probabilistic choice may occur for a variety of reasons such as unobserved attributes of choice alternatives, imprecision of preferences, or random errors/noise in decisions. The Luce choice model (also known as strict utility or multinomial logit) is derived from the choice axiom (also known as the independence from irrelevant alternatives). This axiom postulates that the relative likelihood of choosing one choice alternative A over another choice alternative B is not affected by the presence or absence of other choice alternatives in the choice set. This paper presents a dual choice axiom: the relative probability of NOT choosing A over the probability of NOT choosing B is independent from irrelevant alternatives. A new model of probabilistic choice is derived from this dual axiom. This model coincides with Luce’s choice model only in the case of a binary choice. The new model has similar properties as the Luce choice model: the higher is the utility of a choice alternative, the higher is the probability that a decision maker chooses this alternative and the lower is the probability that he or she chooses any other alternative. The new model differs from the Luce choice model in two aspects: utility of choice alternatives is bounded (from above and below) and choice probabilities are more sensitive to differences in utility of choice alternatives.

Bistable probabilities: a unified framework for studying rationality and irrationality in classical and quantum games.

This article presents a unified probabilistic framework that allows both rational and irrational decision-making to be theoretically investigated and simulated in classical and quantum games. Rational choice theory is a basic component of game-theoretic models, which assumes that a decision-maker chooses the best action according to their preferences. In this article, we define irrationality as a deviation from a rational choice. Bistable probabilities are proposed as a principled and straightforward means for modelling (ir)rational decision-making in games. Bistable variants of classical and quantum Prisoner's Dilemma, Stag Hunt and Chicken are analysed in order to assess the effect of (ir)rationality on agent utility and Nash equilibria. It was found that up to three Nash equilibria exist for all three classical bistable games and maximal utility was attained when agents were rational. Up to three Nash equilibria exist for all three quantum bistable games; however, utility was shown to increase according to higher levels of agent irrationality.

Probabilistic independence axiom

One of the most well-known theories of decision making under risk is expected utility theory based on the independence axiom. The independence axiom postulates that decision maker’s preferences between two lotteries are not affected by mixing both lotteries with the same third lottery (in identical proportions). The probabilistic independence axiom (also known as the cancelation axiom) extends this classic independence axiom to situations when a decision maker chooses in a probabilistic manner (i.e., she does not necessarily prefer the same choice alternative when repeatedly presented with the same choice set). Probabilistic choice may occur for a variety of reasons such as unobserved attributes of choice alternatives, imprecision of preferences, random errors/noise in decisions. According to probabilistic independence axiom, the probability that a decision maker chooses one lottery over another does not change when both lotteries are mixed with the same third lottery (in identical proportions). This paper presents a model of probabilistic binary choice under risk based on this probabilistic independence axiom. The presented model generalizes an incremental expected utility advantage model of Fishburn (Int Econ Rev 19(3):633–646, 1978) and stronger utility model of Blavatskyy (Theory Decis 76(2):265–286, 2014).

Stochastic choice over menus

Models of choice over menus aim at capturing the effect of some behavioral or non-standard element of decision-making on the behavior of a single decision-maker. These models are usually compared with the standard model of choice over menus, in which the decision-maker chooses a menu whose best item is better than that of all other available ones. However, in many empirical settings such as experimental studies, choice data come from a population of decision-makers with possibly heterogeneous attitudes and tastes. This heterogeneity can make the observed choices over menus stochastic. This fact calls for a stochastic characterization of models of choice over menus to be able to better compare and contrast different models empirically. In this paper, I do this task for the standard model, which would be an extension of the random utility model to the realm of choice over menus. In particular, I provide the necessary and sufficient conditions, i.e., axioms on (stochastic) choice data over menus for it to be consistent with a population of decision-makers each of whom behaves according to the standard model. The axioms that characterize the model are the axiom of revealed stochastic preferences over singletons and three rationality axioms.

Random consideration and choice: A case study of “default” options

A growing number of stochastic choice models include a “default” option for situations where the decision maker selects none of the feasible alternatives. While this is a welcome development, these models also present an empirical challenge—since it may be difficult to observe situations where the decision-maker chooses nothing. Taking Manzini and Mariotti’s (2014) independent random consideration model as a point of departure, I investigate what can be learned about models of choice with default when the no-choice behavior of the decision-maker is unobservable.

Bias and Negligence with Freedom of Information

Abstract We analyse decision-making in the presence of Freedom of Information (FOI) rules. A decision-maker chooses whether to acquire costly information to inform his decision regarding a policy action. If information is not disclosed voluntarily a monitor may open a costly investigation, using FOI to access the information. A finding of biased decision-making or negligence in information acquisition generates a reward to the monitor and a penalty to the decision-maker. We find that strengthening FOI to reduce the cost of investigation may increase negligence without necessarily reducing bias. Moreover, increasing the reward for discovering negligence can paradoxically increase negligence in equilibrium.

Hybrid Modelling and Optimization of the Oblique Cutting of AISI 1045 Steel

Optimization is a very important issue in mechanical industry, especially in machining processes, where different aspects must be considered. Thus, selecting the most proper cutting conditions plays a key role for obtaining efficient and competitive products. This article proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95%-confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Two important and conflicting objectives are simultaneously considered: unit cutting time and tool wear rate, which describe the productivity and tool waste, respectively. The outcome of the optimization process is a set of non-dominated solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsen the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.

Multiple stopping time POMDPs: Structural results &amp; application in interactive advertising on social media

This paper considers a multiple stopping time problem for a Markov chain observed in noise, where a decision maker chooses at most L stopping times to maximize a cumulative objective. We formulate the problem as a Partially Observed Markov Decision Process (POMDP) and derive structural results for the optimal multiple stopping policy. The main results are as follows: (i) The optimal multiple stopping policy is shown to be characterized by threshold curves Γl, for l=1,…,L, in the unit simplex of Bayesian Posteriors. (ii) The stopping sets Sl (defined by the threshold curves Γl) are shown to exhibit the following nested structure Sl−1⊂Sl. (iii) The optimal cumulative reward is shown to be monotone with respect to the copositive ordering of the transition matrix. (iv) A stochastic gradient algorithm is provided for estimating linear threshold policies by exploiting the structural results. These linear threshold policies approximate the threshold curves Γl, and share the monotone structure of the optimal multiple stopping policy. (v) Application of the multiple stopping framework to interactively schedule advertisements in live online social media. It is shown that advertisement scheduling using multiple stopping performs significantly better than currently used methods.

Testing probabilistic models of choice using column generation

In so-called random preference models of probabilistic choice, a decision maker chooses according to an unspecified probability distribution over preference states. The most prominent case arises when preference states are linear orders or weak orders of the choice alternatives. The literature has documented that actually evaluating whether decision makers’ observed choices are consistent with such a probabilistic model of choice poses computational difficulties. This severely limits the possible scale of empirical work in behavioral economics and related disciplines. We propose a family of column generation based algorithms for performing such tests. We evaluate our algorithms on various sets of instances. We observe substantial improvements in computation time and conclude that we can efficiently test substantially larger data sets than previously possible.

K-Adaptive Routing for the Robust Network Loading Problem

We experiment an alternative routing scheme for the Robust Network Loading problem. Named k-adaptive, it is based on the fact that the decision-maker chooses k second-stage solutions and then commits to one of them only after realization of the uncertainty. This routing scheme, with its corresponding k-partition of the uncertainty set, is dynamically defined under an iterative method to sequentially improve the solution. The method has an inherent characteristic of multiplying the number of variables and constraints after each iteration, so that additional measures are introduced in the solution strategy in order to control its tractability. We compare our k-adaptive results with the ones obtained through other routing schemes and also verify the effectiveness of the methods utilized using several realistic instances from SNDlib.

Stochastic Choice over Menus

Models of choice over menus aim at capturing the effect of some behavioral or non-standard element of decision-making on the behavior of a single decision-maker. These models are usually compared with the standard model of choice over menus, in which the decision-maker chooses a menu whose best item is better than that of all other available ones. However, in many empirical settings such as experimental studies, choice data come from a population of decision-makers with possibly heterogeneous attitudes and tastes. This heterogeneity can make the observed choices over menus stochastic. This fact calls for a stochastic characterization of models of choice over menus to be able to better compare and contrast different models empirically. In this paper, I do this task for the standard model, which would be an extension of the random utility model to the realm of choice over menus. In particular, I provide the necessary and sufficient conditions, i.e., axioms on (stochastic) choice data over menus for it to be consistent with a population of decision-makers each of whom behaves according to the standard model. The axioms that characterize the model are the axiom of revealed stochastic preferences over singletons and three rationality axioms.

Compromise effect and consideration set size in consumer decision-making

ABSTRACTThe compromise effect dictates that a decision-maker chooses a middle option over an extreme one given a set of choice alternatives since choosing an intermediate option is easier to justify, less likely to be criticized, and is consistent with loss aversion. Our experiment is designed to identify whether the connection between the extremeness of the options and the size of the consideration sets is economically and statistically significant and thus would have important behavioural implications. Specifically, we compare decision-making under small and large consideration sets where the extremeness of the comprising choice options is high, as opposed to low. The results demonstrate that an increase in consideration set size leads to weaker compromise effect (i.e. boundary condition) but when composed of high extremeness, strengthens the compromise effect.