Time-varying Value of Information Acquisition: Learning and Financial Decisions

2021 ◽  
Author(s):  
Shiqi Chen
Keyword(s):  
Information Acquisition ◽  
Value Of Information ◽  
Time Varying ◽  
Financial Decisions

scholarly journals Dynamic information acquisition and time-varying uncertainty

2019 ◽  
Vol 184 ◽  
pp. 104947
Author(s):  
Zhifeng Cai
Keyword(s):  
Information Acquisition ◽  
Time Varying ◽  
Dynamic Information

Diagnostic Reasoning

2017 ◽  
Author(s):  
Björn Meder ◽  
Ralf Mayrhofer
Keyword(s):  
Causal Inference ◽  
Conditional Probability ◽  
Information Acquisition ◽  
Value Of Information ◽  
Probabilistic Inference ◽  
Diagnostic Value ◽  
Diagnostic Reasoning ◽  
Computational Framework ◽  
Single Effect ◽  
Causal Structures

This chapter discusses diagnostic reasoning from the perspective of causal inference. The computational framework that provides the foundation for the analyses—probabilistic inference over graphical causal structures—can be used to implement different models that share the assumption that diagnostic inferences are guided and constrained by causal considerations. This approach has provided many critical insights, with respect to both normative and empirical issues. For instance, taking into account uncertainty about causal structures can entail diagnostic judgments that do not reflect the empirical conditional probability of cause given effect in the data, the classic, purely statistical norm. The chapter first discusses elemental diagnostic inference from a single effect to a single cause, then examines more complex diagnostic inferences involving multiple causes and effects, and concludes with information acquisition in diagnostic reasoning, discussing different ways of quantifying the diagnostic value of information and how people decide which information is diagnostically relevant.

Using trade show information to enhance company success: an empirical investigation

2012 ◽  
Vol 27 (5) ◽  
pp. 384-391 ◽  
Author(s):  
Harriette Bettis‐Outland ◽  
Wesley J. Johnston ◽  
R. Dale Wilson
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Information Acquisition ◽  
Value Of Information ◽  
Multiple Regression Model ◽  
Content Type ◽  
Independent Variables ◽  
Trade Show ◽  
Trade Shows ◽  
The Relationship

PurposeThis paper seeks to provide an exploratory empirical study of the variables that are part of the return on trade show information (RTSI) concept, which is based on the use and value of information gathered at a trade show.Design/methodology/approachThe research is designed to explore relationships and identify those variables that are a particularly important part of the RTSI concept. The paper provides an exploratory test of the relationship between a series of variables that are related to the value of information gathered at trade shows. Data were collected from trade show attendees approximately 60 days after the trade show. A multiple regression model was developed that explores the relationship between the dependent variable that focuses on information value and the independent variables on various aspects of information acquisition, information dissemination, and information use.FindingsThe final multiple regression model found a significant relationship for several variables and has an adjusted R2 value of 0.552. Four significant independent variables were identified – one each in the information use and the shared information categories and two in the information acquisition category. These findings present an interesting picture of how information is used within an organization after it is acquired at a trade show.Research limitations/implicationsThe research is limited by the multiple regression model used to explore the relationships in the data. Also, data from only one trade show were used in the model.Practical implicationsThis paper focuses on the intangible, longer‐term benefits as important considerations when determining the value of new trade show information to the firm. The evaluation of trade show information also should include these intangible benefits, such as improved interdepartmental relations or interactions as well as discussions with other trade show participants in finding new uses for information that impacts the company's future success, as well as shorter‐term benefits such as booth activity.Originality/valueThe paper offers a unique approach for determining the value of information acquired at trade shows. Though information gathering has been included as an outcome variable in previous trade show studies, no other research has studied the value of this new trade show information to the company.

scholarly journals What makes a good query? Prospects for a comprehensive theory of human information acquisition

2021 ◽  
Author(s):  
Björn Meder ◽  
Vincenzo Crupi ◽  
Jonathan D. Nelson
Keyword(s):  
Information Acquisition ◽  
Value Of Information ◽  
Psychological Research ◽  
Good Question ◽  
Comprehensive Theory ◽  
Open Questions ◽  
New Concepts ◽  
Formal Framework ◽  
Short And Long Term ◽  
Advance Theory

Searching for information in a goal-directed manner is central for learning, diagnosis, and prediction. Children continuously ask questions to learn new concepts, doctors do medical tests to diagnose their patients, and scientists perform experiments to test their theories. But what makes a good question? What principles govern human information acquisition and how do people decide which query to conduct to achieve their goals? What challenges need to be met to advance theory and psychology of human inquiry? Addressing these issues, we introduce the conceptual and mathematical ideas underlying different models of the value of information, what purpose these models serve in psychological research, and how they can be integrated in a unified formal framework. We also discuss the conflict between short- and long-term efficiency of prominent methods for query selection, and the resulting normative and methodological implications for studying human sequential search. A final point of discussion concerns the relations between probabilistic (Bayesian) models of the value of information and heuristic search strategies, and the insights than can be gained from bridging different levels of analysis and types of models. We conclude by discussing open questions and challenges that research needs to address to build a comprehensive theory of human information acquisition.

scholarly journals The Wisdom of the Crowd When Acquiring Information Is Costly

2021 ◽  
Author(s):  
Jacob Glazer ◽  
Ilan Kremer ◽  
Motty Perry
Keyword(s):  
Optimal Policy ◽  
Information Acquisition ◽  
Business Strategy ◽  
Investment Decision ◽  
Optimal Investment ◽  
Time Varying ◽  
Information Cascade ◽  
Investment Process ◽  
The Cost

We consider a sequential investment process that is characteristic of crowdfunding platforms, among other contexts. Investors wish to avoid the cost of information acquisition and thus prefer to rely on information acquired by previous investors. This may lead to a phenomenon similar to an information cascade. We characterize the optimal policy that balances between the incentive to acquire information and the optimal investment decision. The policy is based on time-varying transparency levels such that it may be worthwhile to conceal some information in some periods. This paper was accepted by Joshua Gans, business strategy.

Adaptative Model of the Human Operator in a Time-Varying Control Task

1966 ◽  
Author(s):  
E. E. Gould ◽  
K. S. Fu
Keyword(s):  
Human Operator ◽  
Time Varying ◽  
Control Task

A Random Sampling Model of Visual Information Acquisition

1992 ◽  
Author(s):  
Geoffrey Loftus ◽  
Thomas Busey ◽  
John Senders
Keyword(s):  
Information Acquisition ◽  
Random Sampling ◽  
Visual Information
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