Sequential Information Acquisition Research Articles

Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity

This paper studies sequential information acquisition by an ambiguity-averse decision-maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM’s stopping rule is nonmonotonic in beliefs and features randomized stopping. (JEL C61, D81, D83, D91)

Decision Making Under Costly Sequential Information Acquisition: The Paradigm of Reversible and Irreversible Decisions

Decision Making Under Costly Sequential Information Acquisition: The Paradigm of Reversible and Irreversible Decisions

Stock returns, trading volume, and volatility: The case of African stock markets

The presence of the African Stock Markets (ASMs) in the global frontier markets indices confirms their global portfolio diversification role. This study investigates the asymmetric and intertemporal causality among the stock returns, trading volume, and volatility of eight ASMs. Results based on the linear model reveal that return generally Granger cause trading volume. However, evidence from the quantile regression shows that lagged trading volume has a negative causal effect on returns at low quantiles and positive causal effects at high quantiles. This evidence is consistent with volume-return equilibrium models, disposition and overconfidence models, and information asymmetry models. The positive causal effects of volatility on volume support the dispersion of beliefs model. In contrast, intertemporal evidence of contemporaneous and lagged causal relationships from trading volume to volatility supports the mixture of distribution hypothesis, sequential information acquisition hypothesis, and dynamic efficient market hypothesis. Volume-return and return-volume causality dynamics are quantile-specific and therefore driven by market conditions. However, the volume-volatility causality is dependent on volatility regimes. The linear model results confirm how model misspecification can distort and even reverse empirical evidence relative to nonlinear models.

Drilling-Campaign Optimization Using Sequential Information and Policy Analytics

SummaryOptimally designed drilling campaigns are essential for improving oil recovery and value creation. They are required at different stages of the hydrocarbon-field life cycle, including exploration, appraisal, development, and infill. A significant fraction of the revenue risk comes from geological uncertainty, and for this reason, subsurface teams are frequently responsible for optimizing campaign parameters such as the number of wells, the corresponding locations, and the drilling sequence. Companies use the information and learning from drilled wells to adapt the remainder of the campaign, but classical optimization methods do not account for such learning and flexibility over time. Accounting for sequential geological information acquisition and decision making in the optimization of drilling campaigns adds value to the project.We propose a method to optimize drilling campaigns under geological uncertainty by using a sequential-decision model to obtain the optimal drilling policy and applying analytics over the policy to obtain the optimal number of wells and corresponding locations. The novel contribution of policy analytics provides better access to information within the complex data structure of the optimal policy, providing decision support for different decision criteria.The method is demonstrated in two different cases. The first case considers a set of eight candidate wells on predefined locations, mimicking the situation where the method is used after a prior subsurface optimization. The second case considers a set of 12 candidate wells regularly scattered in the same area and uses the method as the first optimization approach to filter out less-attractive regions. Exploiting the geological information on a well-by-well basis improved the expected campaign value by 65% in the first case and by 183% in the second case. The value of spatial geological information and value of flexibility from having more drilling candidates are two byproducts of the method application.

Empathic conversational agents for real-time monitoring and co-facilitation of patient-centered healthcare

Healthcare systems across the world are transitioning into patient-centered healthcare models to ensure improved health outcomes, increased operational efficiencies and respectful patient engagement. Digital health technologies are at the forefront of this transition in facilitating a role for the patient in the clinical dimensions of the healthcare trajectory, from diagnosis and interventions to treatment and recovery. Despite this prevalence in the clinical space, the non-clinical needs of patient mental health and wellbeing are frequently overlooked by contemporary patient-centered healthcare models. Conversational agents (or chatbots) are digital dialogue systems that are widespread and widely used in sequential information provision and information acquisition tasks. Given the intimate nature of this human-machine interaction, conversational agents can be effectively utilized to support and sustain patient mental health and wellbeing. In this paper, we propose an empathic conversational agent framework based on an ensemble of natural language processing techniques and artificial intelligence algorithms for real-time monitoring and co-facilitation of patient-centered healthcare for improved mental health and wellbeing outcomes. The technical contributions of this framework are; detection of patient emotions, prediction of patient emotion transitions, detection of group emotions, formulation of patient behavioral metrics, and resource recommendations based on patient concerns. The architectural contributions of the framework are intelligent communication channels that stream empathic conversational elements and resource recommendations for the multi-user conversations and co-facilitation updates for the human healthcare provider interface. The framework was empirically evaluated on a benchmark dataset and further validated based on a clinical protocol designed for its application in an online support group setting for cancer patients and caregivers in Canada. The results of these experiments confirm the effectiveness of this framework, its contributory role and practical value in realizing a patient-centered healthcare model for improved mental health and wellbeing outcomes.

Information Overload Effects in Sequential Information Acquisition for Investment Decision-Making

This paper experimentally investigates structured investment decision-making with sequential information provision. Subjects were asked to rank investment alternatives using a scoring model. A mixed design with information provision as the within-subject variable and initial information load as the between-subjects variable was employed. A computer-based process tracing technology provided insights into the underlying information acquisition process – information cues were covered by boxes for the second step of the decision. Moving the cursor over a box uncovered the information. Duration and frequency of uncovering the different information cues was recorded in a database. The main variables of interest were decision accuracy, decision confidence, and measures characterizing the information acquisition process. The main findings were the following: the amount of information provided in the first step significantly impacted decision accuracy and decision confidence, which decreased for the high initial information load group. In addition, receiving a high number of information cues in the first step led to a reduction in the attention devoted to the information received in the second step. Confirmatory information search behavior could not be observed. Findings help further our understanding on how the amount and timing of information provision impact decision quality and confidence.

Deliberating Collective Decisions

We present a dynamic model of sequential information acquisition by a heterogeneous committee. At each date, agents decide whether to vote to adopt one of two alternatives or continue to collect more information. The process stops when a qualified majority vote for an alternative. Three main insights emerge from our analysis and are consistent with an array of stylized facts regarding committee decision making. First, majority rule is more vulnerable than super-majority rules to the disproportionate influence of impatient committee members. Second, more diverse preferences, more patient members, or more unanimous decision voting rules lead to lengthier deliberation and more accurate decisions. Finally, balanced committees unanimously prefer to delegate deliberation power to a moderate chairman rather than be governed by a rule such as unanimity.

The Economics of Information in Transport

Travelers often are incompletely informed about travel alternatives, which has important implications for various domains of travel behavior such as whether or not to make a trip, modal choice, the timing of a trip or route choice. During the last decade large efforts have been made to increase the availability of information to travelers by means of advanced traveler information systems (ATIS). This paper reviews economic aspects of information in transport markets. First, I will discuss information acquisition from an economics perspective by characterizing costs and benefits of information, leading to the formulation of optimal strategies to acquire information. This will be done in the context of search strategies leading to sequential information acquisition. I further discuss the broader consequences of information acquisition on the functioning of transport networks. In congested networks, when travelers change their behavior on the basis of information they obtain, this will have consequences not only for their own travel times, but also those of other travelers. This leads to interesting positive (and possibly negative) spillovers having important policy implications. The next step is the analysis of a traveler's choice whether to adopt advanced traveler information systems, implying the derivation of a demand function for information.

An optimal sequential information acquisition model subject to a heuristic assimilation constraint

Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach – The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings – The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs. Originality/value – The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered.

Modeling Sequential Information Acquisition Behavior in Rational Decision Making

ABSTRACTMost real‐life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real‐valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments.

Discovering Statistics Using IBM SPSS Statistics. And Sex and Drugs and Rock'n'Roll

BuchbesprechungenDiscovering Statistics Using IBM SPSS Statistics. And Sex and Drugs and Rock'n'RollPublished Online:November 21, 2014https://doi.org/10.1024/1012-5302/a000397PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreFiguresReferencesRelatedDetailsCited byEnhancing Online Instructional Approaches for Sustainable Business Education in the Current and Post-Pandemic Era: An Action Research Study of Student Engagement31 December 2022 | Education Sciences, Vol. 13, No. 1The Use of Language in Solving Inverse Algebraic Functions Problems27 April 2022 | Frontiers in Education, Vol. 7Engineering a Better IT Program Manager: A Comparative Study of IT PM Education and Training1 April 2021 | Defense Acquisition Research Journal, Vol. 28, No. 96Engineering a Better IT Program Manager: A Comparative Study of IT PM Education and Training1 April 2021 | Defense Acquisition Research Journal, Vol. 28, No. 96Screening for Early Signs of Paternal Perinatal Affective Disorder in Expectant Fathers: A Cluster Analysis Approach23 December 2020 | Journal of Personalized Medicine, Vol. 11, No. 1A multidimensional approach to motives for studying explains differences in student wellbeing: Validation of the motivational mindset modelSSRN Electronic JournalInformation Overload Effects in Sequential Information Acquisition for Investment Decision-MakingSSRN Electronic JournalThe effects of psychological trauma on psychopathy1 August 2019 | Forensic Update, Vol. 1, No. 131Sex differences in cardiovascular responses to orthostatic challenge in healthy older persons: A pilot studyPhysiology International, Vol. 106, No. 3National Culture and Bank DepositsSSRN Electronic JournalExamining the Impact of Audit Firms' Use of Skepticism Feedback in Auditor TrainingSSRN Electronic JournalA Contingent Perspective on Management Accounting Systems in Family FirmsSSRN Electronic JournalPredicting Delinquency through Psychosocial and Environmental Variables among Immigrant and Native-Born AdolescentsSSRN Electronic JournalUntersuchung Zum Zusammenhang Zwischen Heimtierbesitz Und Menschlicher Gesundheit Bei lteren Personen in Deutschland (Investigation of the Relationship between Pet Ownership and Health of Elderly People in Germany)SSRN Electronic JournalNational Culture and Bank Risk-TakingSSRN Electronic JournalBoard Processes, Cognitive Conflicts and Financial Reporting QualitySSRN Electronic JournalAuditor Sensitivity to Real Earnings Management: An Experimental InvestigationSSRN Electronic Journal Volume 27Issue 6December 2014ISSN: 1012-5302eISSN: 1664-283X InformationPflege (2014), 27, pp. 430-430 https://doi.org/10.1024/1012-5302/a000397.© 2014Hogrefe AGPDF download

An optimal information acquisition model for competitive advantage in complex multiperspective environments

The optimal information acquisition process is a major strategic task for sustaining a firm’s competitive advantage. We define the optimal sequential information acquisition behavior of a rational decision maker (DM) when allowed to acquire two pieces of information from and observe positive credible signals on a set of multidimensional products. We illustrate how firm reputation affects the continuity of the expected utilities derived from a given search and may generate reversals in the information acquisition incentives of DMs when deciding whether or not to shift their search processes between different signal-induced markets. This study makes a number of important contributions to our understanding of a firm’s information acquisition. First, it provides a formal analysis of the information acquisition process when the characteristics defining a product have a continuous set of variants. Second, it allows for the study of risk-averse DMs, while most of the literature concentrates on risk-neutral DMs. Third, it opens the way for strategic scenarios to be considered when analyzing the information acquisition processes of firms and creates a direct link to the game theoretical literature on strategic reporting. Fourth, it can be easily implemented within multicriteria decision making methods such as the analytic hierarchy process (AHP) to study the information acquisition behavior of DMs when the characteristics of the products are unknown.

The optimal sequential information acquisition structure: A rational utility-maximizing perspective

We consider a rational utility maximizer decision maker (DM) who must gather two pieces of information from a set of multidimensional products before making a choice. We analyze the resulting sequential information acquisition process where the DM tries to find the best possible product subject to his information acquisition constraint. In addition, we introduce publicly observable signals that allow the DM to update his expected utility functions following a standard Bayesian learning rule. Even though it seems intuitively plausible to assume that the transmission of positive and credible information may lead DMs to accept any product signaled more eagerly, this paper illustrates how transmitting credible positive information is not sufficient to decrease the rejection probability faced by the information sender on its set of products. A significant difference in product rejection probabilities arises depending on the characteristic on which signals are issued, as will be illustrated numerically for both risk-neutral and risk-averse DMs.

Decreasing Marginal Value of Information Under Symmetric Loss

We investigate conditions under which the value of additional information is diminishing in a setting where the decision maker has access to multiple, identically-distributed, information signals. The signals are assumed to be independent conditional on an unknown payoff-relevant parameter. The decision maker minimizes a quadratic loss function. Quadratic losses arise in quality control, scoring rules, and other applications. We characterize two concepts of diminishing marginal value of information. The first is an ex ante concept, before any information is observed, and the second is an ex post concept, after observation of previous information signals. The former concept is useful for ex ante information acquisition decisions and the latter for sequential information acquisition.

Time course of information processing in visual and haptic object classification.

Vision identifies objects rapidly and efficiently. In contrast, object recognition by touch is much slower. Furthermore, haptics usually serially accumulates information from different parts of objects, whereas vision typically processes object information in parallel. Is haptic object identification slower simply due to sequential information acquisition and the resulting memory load or due to more fundamental processing differences between the senses? To compare the time course of visual and haptic object recognition, we slowed visual processing using a novel, restricted viewing technique. In an electroencephalographic (EEG) experiment, participants discriminated familiar, nameable from unfamiliar, unnamable objects both visually and haptically. Analyses focused on the evoked and total fronto-central theta-band (5–7 Hz; a marker of working memory) and the occipital upper alpha-band (10–12 Hz; a marker of perceptual processing) locked to the onset of classification. Decreases in total upper alpha-band activity for haptic identification of objects indicate a likely processing role of multisensory extrastriate areas. Long-latency modulations of alpha-band activity differentiated between familiar and unfamiliar objects in haptics but not in vision. In contrast, theta-band activity showed a general increase over time for the slowed-down visual recognition task only. We conclude that haptic object recognition relies on common representations with vision but also that there are fundamental differences between the senses that do not merely arise from differences in their speed of processing.

Information Acquisition and Sharing in a Vertical Relationship

Suppliers are increasingly acquiring consumer information in a sequential fashion with negligible marginal costs, and influencing downstream retailer actions by sharing the acquired information using either exante mandatory or ex-post voluntary sharing formats. This paper examines the interaction between a manufacturer's optimal strategies for sequential information acquisition on product fit/quality and for information sharing in a vertical relationship. We examine how the flexibility to sequentially control information collection, and how the flexibility in ex-post voluntary information sharing, may influence the manufacturer's equilibrium amount of information acquired. We show that, when information acquisition is sequential, the manufacturer may not acquire perfect information even if it is costless to do so. This self-restriction in information acquisition follows from the manufacturer's motivation to strategically influence retail behavior. When information acquisition is inflexible and constrained to be either zero or perfect information, the manufacturer will acquire more (less) information under voluntary (mandatory) sharing. Nevertheless, voluntary information sharing will unambiguously lead to more information being generated, because the manufacturer has the option to strategically withhold the disclosure if the acquired information turns out to be unfavorable. Finally, the conditions under which the manufacture prefers a particular sharing format are examined.

Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies

We study the sequential information acquisition problem for rule-based expert systems as follows: find the information acquisition strategy that minimizes the expected cost to operate the system while maintaining the same output decisions. This problem arises for rule-based expert systems when the cost or time to collect inputs is significant and the inputs are not known until the system operates. We develop several “optimistic” heuristics to generate information acquisition strategies and study their properties. The heuristics provide choices concerning precision (i.e., how optimistic) versus computational effort. The heuristics are embedded into an informed search algorithm (based on AO*) that produces an optimal strategy and a greedy search algorithm. The search strategies are designed for situations in which rules can overlap and the cost of collecting an input may depend on the set of inputs previously collected. We study the properties of these approaches and simulate their performance on a variety of synthetic expert systems. Our results indicate that the heuristics are very precise, leading to near optimal results for greedy search and moderate search effort for optimal search.

The role of user capability and incentives in group and individual decision support systems: An economics perspective

We model the decision making processes in decision support systems and programs as sequential information acquisition processes and compare their usefulness. A Bayesian decision maker is shown to be indifferent between the two approaches. In contrast, a decision maker with bounded rationality prefers the decision support systems approach. The model is extended to group decision support systems where the interaction between the decision makers and the group facilitator is modelled as a non-cooperative economics game. We show that in some instances the group facilitator would prefer precommitment to an interaction plan rather than allow evolutionary planning of the interaction. This planning is similar to that in a program and may take the form of an organization chart.

Inductive Expert System Design: Maximizing System Value

There is a growing interest in the use of induction to develop a special class of expert systems known as inductive expert systems. Existing approaches to develop inductive expert systems do not attempt to maximize system value and may therefore be of limited use to firms. We present an induction algorithm that seeks to develop inductive expert systems that maximize value. The task of developing an inductive expert system is looked upon as one of developing an optimal sequential information acquisition strategy. Information is acquired to reduce uncertainty only if the benefits gained from acquiring the information exceed its cost. Existing approaches ignore the costs and benefits of acquiring information. We compare the systems developed by our algorithm with those developed by the popular ID3 algorithm. In addition, we present results from an extensive set of experiments that indicate that our algorithm will result in more valuable systems than the ID3 algorithm and the ID3 algorithm with pessimistic pruning.

An economics perspective on the usefulness of decision support systems

Decision making in a decision support system is modeled as a sequential information acquisition process with noisy information sources and costly information assimilation. The net benefit from using a Decision Support Systems approach is compared to that from a Programs approach. DSS provides greater benefits for ad-hoc decisions and this advantage increases as the information sources become noisier. Programs are preferred for recurrent decisions. Increased assimilation costs decrease the number of alternatives examined.