A rational information gathering account of infant habituation.

Gaze is one of the primary experimental measures for studying cognitive development, especially in preverbal infants. However, the field is only beginning to develop a principled explanatory framework for making sense of the various factors affecting gaze. We approach this issue by addressing infant gaze from first principles, using rational information gathering. In particular, we revisit the influential descriptive account of Hunter and Ames (1988) (H&A), which posits a set of regularities argued to govern how gaze preference for a stimulus changes with experience and other factors. When the H&A’s model is reconsidered from the perspective of rational information gathering (as recently also proposed by other authors), one feature of the model emerges as surprising: that preference for a stimulus is not monotonic with exposure. This claim, which has at least some empirical support, is in contrast to most statistical measures of informativeness, which strictly decline with experience. We present a normative, computational theory of visual exploration that rationalizes this and other features of the classic account. Our account suggests that H&A’s signature nonmonotonic pattern is a direct manifestation of a ubiquitous principle of the value of information in sequential tasks, other consequences of which have recently been observed in a range of settings including deliberation, exploration, curiosity, and boredom. This is that the value of information gathering, putatively driving gaze, depends on the interplay of a stimulus’ informativeness (called Gain, the focus of other rationally motivated accounts) with a second factor (called Need) reflecting the estimated chance that information will be used in the future. This computational decomposition draws new connections between infant gaze and other cases of exploration, and offers novel, quantitative interpretations and predictions about the factors that may impact infant exploratory attention.

The value of information (VOI) methodology can be used for determining whether further information should be collected before making a decision. Typically, a VOI is calculated on an expected monetary value (EMV) basis by means of a decision tree, and the cost of the information is compared to the VOI to determine whether to undertake further data collection. A majority of VOI studies employ the discrete decision tree approach to VOI evaluation, thus simplifying the problem by reducing the range of the outcomes and the number of uncertainties addressed at the same time. In order to overcome and address the simplifications introduced when performing a discrete VOI evaluation, a Monte Carlo approach founded on Bayesian decision theory can be applied. This increases computational complexity, but allows for a full uncertainty description of range variables such as oil in place (OIP) and can be integrated with quantitative prospect evaluation methods. The Monte Carlo VOI (MCVOI) approach is presented and compared to the discrete decision tree approach by means of an appraisal well decision. In addition, a complete MCVOI workflow is proposed. The paper aims at familiarizing VOI practitioners with the MCVOI approach by explaining how it works and by illuminating its benefits, such as eased expert assessment and getting past discretization of variables that are inherently continuous. The paper also places the VOI approach in a risk management context, thus extending VOI methodology beyond the pure calculation of a VOI number. Introduction One of the most useful features of decision analysis is its ability to distinguish between constructive and wasteful information gathering. VOI analysis evaluates the benefits of collecting additional information prior to making a decision. Such information gathering may be worthwhile if it holds the possibility of changing the decision that would be made without further information. The majority of VOI applications in the oil and gas industry are based on a discrete approach whereby the uncertainties, both the ones we hope to learn about but cannot directly observe, and the information gathering results, are discretized into a finite number, usually 2-3, of degrees.[1] Although this discretization is sufficient in many situations, continuous representations of the uncertainties may be more suitable for others, such as the uncertainty in oil in place (OIP) or the production in a given year. For some combinations of prior probability distributions and likelihood functions, representing the current information and the confidence related to new information, respectively, Bayesian updating of the probabilities (to get the posterior) is straightforward. Conjugate priors are families of distributions that ease the computational burden when used as prior distributions. Given a conjugate prior, there is a set of likelihood functions for which there exist simple formulas for calculating the posterior distribution. Hence, if the analyst believes that one of these conjugate priors and its associated likelihood functions adequately describe the uncertainties, the probability updating part of the VOI analysis is trivial.

In product development, firms choose product concepts in the conceptual design phase and develop final products from the chosen concepts. Concept selection is one of the most difficult decisions in product development since it involves large degrees of uncertainties. This paper presents a framework for decision-analytic concept selection and information gathering in a public project, in which the government has an option to cancel the project if the cost of the project exceeds the budget. In this framework, a customer (e.g., the government) has an option to cancel the project instead of a decision-maker (e.g., a national laboratory). In information gathering, this paper presents, first, sensitivity analysis that enables engineers to investigate whether it is beneficial to collect additional information about uncertainties, and second, the value of perfect information in determining the maximum monetary resource they should spend for such activity. Finally, an illustrative example demonstrates decision-analytic concept selection, sensitivity analysis, and the value of perfect information for a next generation linear collider.Copyright © 2005 by ASME

What is the benefit from obtaining more precise values of environmental or other public goods through surveys or other information gathering? In the value of information (VOI) problem studied here, a buyer who wishes to preserve a resource sets a price to offer a seller without knowing precisely its protection value, B, nor its value to the seller, V. The VOI from more precise information about B is important for environmental and natural resource valuation, but is typically not quantified nor compared to valuation costs. More precise environmental values reduce the frequency of two types of mistakes (protecting the resource when it should not be; and not protecting it when it should), and increases ex ante welfare. We apply our analysis to Amazon rainforest protection, focusing on the “value of perfect information,” VOPI, which, we show through simulations, typically exceeds realistic valuation costs, justifying significant valuation expenditures. VOPI also depends on the nature of buyer–seller interactions, and takes its highest value when the buyer has full concern for the seller’s outcome. Our paper proposes and prepares the base for a new, needed, field in applied welfare economics, the “benefit–cost analysis of public-good valuation studies.”

Civil infrastructure systems form the backbone of modern civilization, providing the basic services that allow society to function. Effective management of these systems requires decision-making about the allocation of limited resources to maintain and repair infrastructure components and to replace failed or obsolete components. Making informed decisions requires an understanding of the state of the system; such an understanding can be achieved through a computational or conceptual system model combined with information gathered on the system via inspections or sensors. Gathering of this information, referred to generally as sensing, should be optimized to best support the decision-making and system management processes, in order to reduce long-term operational costs and improve infrastructure performance. In this work, an approach to optimal sensing in infrastructure systems is developed by combining probabilistic graphical models of infrastructure system behavior with the value of information (VoI) metric, which quantifies the utility of information gathering efforts (referred to generally as sensor placements) in supporting decision-making in uncertain systems. Computational methods are presented for the efficient evaluation and optimization of the VoI metric based on the probabilistic model structure. Various case studies on the application of this approach to managing infrastructure systems are presented, illustrating the flexibility of the basic method as well as various special cases for its practical implementation. Three main contributions are presented in this work. First, while the computational complexity of the VoI metric generally grows exponentially with the number of components, growth can be greatly reduced in systems with certain topologies (designated as cumulative topologies). Following from this, an efficient approach to VoI computation based on a cumulative topology and Gaussian random field model is developed and presented. Second, in systems with non-cumulative topologies, approximate techniques may be used to evaluate the VoI metric. This work presents extensive investigations of such systems and draws some general conclusions about the behavior of this metric. Third, this work presents several complete application cases for probabilistic modeling techniques and the VoI metric in supporting infrastructure system management. Case studies are presented in structural health monitoring, seismic risk mitigation, and extreme temperature response in urban areas. Other minor contributions included in this work are theoretical and empirical comparisons of the VoI with other sensor placement metrics and an extension of the developed sensor placement method to systems that evolve in time. Overall, this work illustrates how probabilistic graphical models and the VoI metric can allow for efficient sensor placement optimization to support infrastructure system management. Areas of future work to expand on the results presented here include the development of approximate, heuristic methods to support efficient sensor placement in non-cumulative system topologies, as well as further validation of the efficient sensing optimization approaches used in this work.

One of the most useful features of decision analysis is its ability to distinguish between constructive and wasteful information gathering. Value-of-Information (VOI) analysis evaluates the benefits of collecting additional information before making a decision. VOI models describe the relationship between the uncertain quantities of interest, the reliability of the information and the decision criteria. Many uncertainty quantities are continuous in nature and the probability distributions that describe their uncertainty are discretized, and presented in decision trees, to simplify analysis. A three-point discretization of a continuous distribution is standard, and often preserves the main characteristics (central tendency, spread) of distributions that are close to symmetric. However, VOI studies rarely, if ever, include an analysis on the sensitivity of the VOI to the quality of the discretization. In this work we investigate a variety of discretization techniques, for a range of typical information gathering situations. The investigation utilizes a robust model that accurately calculates the VOI for any combination of continuous distributions. The key criterion for assessing a discretization techniques is whether or not they it has a significant impact on the decision to collect the information. The goal of the work is to provide practical guidance on the level and technique of discretization required. Introduction. Most of what petroleum engineers or geoscientists do involves "acquiring" information, with the aim of improving decision-making. "Information" is used here in a broad sense to cover such activities as acquiring of data, performing technical studies, hiring consultants, or performing diagnostic tests. In fact, other than to meet applicable regulatory requirements, the main reason for collecting any information, or doing any technical analysis, should be to make better decisions. The fundamental question for any information-gathering process is then whether the likely improvement in decision-making is worth the cost of obtaining the information. This is the question that the VOI technique is designed to answer. The oil and gas literature includes a number of papers (e.g. Begg et al, 2002; Bratvold et al, 2008) and books (e.g. Bratvold and Begg, 2010; Newendorp and Schuyler, 2003) where VOI analysis is introduced and described in detail. It is common to use decision trees to structure and evaluate the VOI decision. If the underlying uncertainty is continuous in nature, one of the common discretization methodologies such as Extended Swanson-Megill, Extended Pearson-Tukey, or the McNamee-Celona is often used to discretize the underlying uncertainty into a few, usually 2 or 3, degrees(Bickel et al. 2011). In general, the discrete probabilities and values are selected with the aim of matching the moments (mainly the mean and variance) of the continuous representation. In this paper we refer to this approach as the Low Resolution Decision Tree (LRDT) approach. A few papers discuss the calculation of VOI when the uncertain event of interest is continuous (Chavez & Henrion, 2004; Arild, Lohne, and Bratvold, 2008; Bickel, 2012). However, the VOI calculation approach in these papers is presented using different terminology than what is used in the LRDT approach. Furthermore, the papers are either limited to relatively simple problems or have very different representations of the reliability or quality of the information gathered. Despite the fact the LRDT approach can have significant errors, VOI calculation are rarely conducted using the full continuous representation of the uncertain event of interest. We suspect that the reason lies in the issue discussed above.

Use of Outcomes Information in Child Mental Health Treatment: Results From a Pilot Study

We explore how the economically optimal selection of environmental policy instruments is influenced by information available to decision-makers. We also investigate the value of different types of information for environmental management. The focus is on nonpoint source water pollution regulation in the Susquehanna River Basin of Pennsylvania. An extended abstract: Imperfect information about costs and benefits can greatly complicate policy decisions to protect and restore water resources. This has become very apparent to water quality managers in the U.S. in recent years, as they have struggled to comply with the U.S. Environmental Protection Agency's Total Maximum Daily Load regulations. The slow progress has been attributed in large degree to the fact that key information for assessing the condition of streams, lakes, and estuaries, developing sensible plans to restore impaired waters was unavailable and costly to obtain [NRC 2000]. The relationship between agricultural production and damages from water pollution is complex, involving multiple physical and biological links that are not perfectly understood. For example, transport of pollutants off a field to water body depends on stochastic weather events and privately known management practices. The change in water quality, as measured by physical and biological indicators, in response to discharge of agricultural pollutants, is not completely known. Decision-makers have also significant uncertainty about pollution abatement costs, and even more so about economic benefits of water quality improvement. Choices about data collection and analysis to reduce uncertainty and improve water quality programs should be guided by the value of the information for management relative to costs. In this research we examine the value of different types of information, and the effect of information collection on environmental policy performance for the management of agricultural water pollution in the Susquehanna River Basin (SRB) (Pennsylvania). The analysis is based on a model that simulates the effects of water pollution control instruments on polluters' resource allocation decisions, the costs the polluters incur from changes in resource allocation, and the effects of polluters' choices on pollution loads. We group all uncertain factors into three broad categories: economic uncertainty about polluters' abatement costs, hydrologic uncertainty related to the effect of changes in agricultural practices on pollution loads, and damage cost uncertainty, which includes biophysical responses of the water body to pollution and economic valuation of the response. The imperfect knowledge is captured by randomizing the values of model parameters, which affect abatement costs, pollution load resulting from agricultural practices, and the benefits of load reductions. We model five information collection strategies: 1 - no additional information is collected; 2 - 4 - hydrologic, economic or damage uncertainty is resolved; and 5 - perfect information about all uncertain parameters is collected. For each information collection strategy, two types of policy instruments - input taxes and quantity controls - are considered. The expected net benefit maximization is used as criteria for estimating environmental policy performance. The value of information collection is estimated as the expected gain in policy performance due to utilizing information. The results show that performance of price and quantity control differs significantly when economic information is not available, with price mechanisms always outperforming the quantity control. The value of information to the large extent depends on the instrument used in environmental policy. The value of information is greater for quantity controls than for price mechanisms. Even in the case when no additional information is collected, introduction of price control results in significant increase in expected net benefits, while quantity control brings smaller expected net benefit gain. When perfect information is collected, price and quantity instruments perform the same. As a result, the expected increase in net benefits due to information gathering (i.e. the value of information) is smaller for price control than for the quantity control. Information about pollution damage has the highest value for both quantity and price controls. An interesting result is that economic information is more valuable for environmental policy than hydrologic information. Environmental pollution is an economic activity, and addressing the problem requires analysis of environmental policy effects on economic choices (which, in turn, determine pollution loads) and the study of the costs of changes in resource allocation. Our analysis confirms that economic information on both benefits and costs is essential for sound environmental policy design. Reference: National Research Council (NRC). 2000. Assessing the TMDL Approach to Water Quality Management. National Academy Press, Washington, DC.

Expected economic value of the information provided by fishery research surveys

DIGS (Decision Support Information Gathering System) uses the value of information to guide the information gathering process and uses the gathered information to provide the decision recommendations to the human users. DIGS uses an influence diagram as a modeling tool. In this paper, we create a model to represent the investment scenario of a novice stock investor. By using the sequential myopic information gathering technique, DIGS generates a sequence of information gathering actions. The actions are dependent on each other in that the action DIGS executes at time t/sub 1/ will be based on the results of the pervious action at time t/sub 0/. DIGS also employs a stopping mechanism for the information gathering actions based on the information value and time constraints. Thus, DIGS can be used as an anytime system. Compared to a pre-generated sequence of actions, our technique has the flexibility to react to the gathered information, and to use it to guide the subsequent gathering actions. Therefore, our system can adapt to the newly acquired information and avoid the computational complexity of planning the series of actions in advance.

To maximize its profit, a firm has to design a product or a system and sell it at the price that maximizes its expected profit. The selling price has a direct influence on the demand for the product. Consequently, corporations spend vast amounts of money and resources on gathering information to improve demand forecasting. This paper investigates the behavior of the value of information (VOI) for uncertain demand, and the effects of demand elasticity for the product on the information value. We discuss the variation of the VOI with demand elasticity and show that VOI is maximum at the specific elasticity at which the decision maker is indifferent between two selling price alternatives. The results enable firms to determine the value of demand information and the effects of demand elasticity for a selling price.

This paper develops a method for estimating the value of additional information to the individual livestock producer. In doing so it considers as part of the decision made by the farmer to vaccinate animals against B. bovis the decision to collect information on the health status of his herd using serological sampling. Bayesian decision theory is used in this paper. Bayesian decision theory combines statistical and economic information to assist in identifying optimal management policies. This approach has been used in a number of situations in animal health decision making, for example Williamson, 1975; Elder and Morris, 1986; Fetrow et al., 1985; Parsons et al., 1986. This paper firstly examines the private use of animal health information then the relationship between the cost of gathering information and the value of the information. This is followed discussion about decisions to gather additional information. A method to determine the optimal sample size is examined and applied.

After the close of an auction, the winning bidder may find that he is unable to carry out his bid offer. This paper seeks to determine what measures the seller should take to maximize his share of the surplus when bidders are privately informed about their risk of default. Special attention is paid to the effect of imposing a default penalty, the value of gathering information about each bidder's default risk, and the role of commitment. It is shown that the value of gathering information is negligible when the seller has commitment power and negative when the seller lacks commitment power. When the seller is informed about each bidder's risk, the seller benefits from the ability to commit. However, when the seller is uninformed, he is able to capture nearly all the surplus independent of whether or not he has commitment power.

Risk is inherent to all phases of a petroleum field lifetime due to geological, economic and technological uncertainties, which are very significant on oil recovery in development phase, the focus of this work. The acquisition of additional information of uncertain attributes and flexibility during the development are key points to risk mitigation. The Value of Information (VoI) is used to quantify the benefits of new information, giving more accuracy to the project. The Value of Flexibility (VoF) measures the benefits of adding flexibility to the project considering different possible scenarios. A new and reliable methodology has been proposed to quantify VoI and VoF based on the decision tree technique in order to combine the uncertain attributes. All reservoir models generated by the tree are submitted to parallel simulation and Geological Representative Models (GRM) are selected to represent geological uncertainties. The methodology includes the criteria used for selection of GRM, optimization of production strategies of each GRM considering the gathering of additional information and statistical treatment of the results. The methodology has been applied in a decision-making process of a giant offshore petroleum field. The field has been developed by blocks due to its physical limitations and intrinsic characteristics and the high investment necessary to develop a giant field. The contributions of this work are (1) to show the importance of VoI and VoF concepts in decision-making process in petroleum field development and the complexity of this type of decision, (2) to apply the proposed methodology in a giant offshore field modeled by parts, minimizing risks associated to the development of this type of field and (3) to evaluate the importance of the reservoir uncertainties in risk mitigation. An additional important contribution is to present the details of the use of reservoir simulation in the process, trying to obtain the best relationship between computation effort and reliability of the decision making process.

The management of civil infrastructure involves accounting for uncertain or variable factors which influence the performance of a system. These factors can vary in space over the domain of the system and in time as the system changes. Effective decision-making for system management should be guided by models which account for uncertainty in these influencing factors as well as information gathered about the system to reduce this uncertainty. Value of information provides a rational metric for quantifying the benefits of information gathering efforts to support system management decision-making. However, the computation of this metric in spatially and temporally extensive systems presents a practical impediment to its implementation. In this paper, we investigate a special case of system topology, termed as a temporally decomposable system with uncontrolled evolution, in which the computational complexity of value of information evaluation grows at a manageable rate with respect to the problem time horizon. We demonstrate the evaluation of the value of information to support the design of a structural health monitoring scheme, using data collected for the Scott Hall building at Carnegie Mellon University. We also investigate the relative benefits of online sensor placement, i.e., of having the ability to revise sensor placements and/or schedules over time as information is gathered within a system.