True State Of Nature Research Articles

The functional aspects of selective exposure for collective decision-making under social influence

Opinion diversity is crucial for collective decision-making, but maintaining it becomes challenging in the face of social influence. We propose selective exposure as an endogenous mechanism that preserves opinion diversity by forming exclusive subgroups of like-minded individuals, or echo chambers, which have been often perceived as an obstacle to achieving collective intelligence. We consider situations where a group of agents collectively make decisions about the true state of nature with the assumption that agents update their opinions by adopting the aggregated opinions of their information sources (i.e., naïve learning), or alternatively, replace incongruent sources with more like-minded others without adjusting their opinions (i.e., selective exposure). Individual opinions at steady states reached under these dynamics are then aggregated to form collective decisions, and their quality is assessed. The results suggest that the diversity-reducing effects of social influence are effectively confined within subgroups formed by selective exposure. More importantly, strong propensities for selective exposure maintain the quality of collective decisions at a level as high as that achieved in the absence of social influence. In contrast, naïve learning allows groups to reach consensuses, which are more accurate than initial individual opinions, but significantly undermines the quality of collective decisions.

Discovering Influencers in Opinion Formation Over Social Graphs

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).

Robust incentives for information acquisition

A principal needs to make a decision, and contracts with an expert, who can obtain information relevant to the decision by exerting costly effort. The principal can incentivize effort by paying a reward based on the expert's reported information and on the true state of nature, which is revealed ex post. Both parties are financially risk-neutral, and payments are constrained by limited liability. The principal is uncertain about the expert's information acquisition technology: she knows some actions (experiments) that he can take to obtain information, but there may also be other experiments available. The principal seeks robustness to this uncertainty, and so evaluates any incentive contract using a worst-case criterion. Under quite general conditions, we show that the optimal contract is a restricted-investment contract, in which the expert chooses from a subset of the decisions available to the principal, and is then rewarded proportionally to the value of his designated decision in the realized state.

Science and Human Animality in Mary Shelley’sFrankenstein

Science and Human Animality in Mary Shelley’s<i>Frankenstein</i>

The value and motivating mechanism of transparency in organizations

The question how transparency in organizations affects performance has received considerable interest from researchers in management, psychology, and organization science. The widely held view is that transparency benefits organizational performance, because it reduces employee uncertainty. However, causal empirical evidence on the value of transparency and its motivating mechanism is still scarce. In this paper, we report the findings from an experiment, in which an agent has only probabilistic beliefs about the true state of nature and needs to choose costly effort that benefits the principal. The true state relates to his fixed-wage, which can either be high or low. The principal needs to decide whether to create Informative Transparency by disclosing the true state to the agent via a costly, fixed-form message. Our results show a considerable value of transparency: even if transparency involves the disclosure of ‘bad news’ (the low state), effort almost doubles relative to non-disclosure. Looking at the motivating mechanism, we do not find that transparency motivates primarily because it reduces uncertainty for the agent. Instead, we find that Uninformative Transparency that merely involves communication of already known facts is equally effective. Many principals, however, misperceive the value of transparency and disclose information too restrictively.

Coalitional fairness with participation rates

This paper investigates coalitional fairness in pure exchange economies with asymmetric information. We study allocations of resources which are immune from envy when comparisons take place between coalitions. The model allows negligible and non-negligible traders, partially informed about the true state of nature at the time of consumption, to exchange any number, possibly infinite, of commodities. Our analysis is based on the Aubin approach to coalitions and cooperation. Namely, on a notion of cooperation allowing traders to take part in one or more coalitions simultaneously, employing only shares of their endowments (participation rates). We introduce and study in detail the notion of coalition fairness with participation rates (or Aubin c-fairness) and show that flexibility in cooperation permits to recover the failure of fairness properties of equilibrium allocations. Our results provide applications to several market outcomes (ex-post core, fine core, ex-post competitive equilibria, rational expectations equilibria) and emphasize the consequences of the convexification effect due to participation rates for models with large traders and infinitely many commodities.

Understanding the Role of P Values and Hypothesis Tests in Clinical Research

P values and hypothesis testing methods are frequently misused in clinical research. Much of this misuse appears to be owing to the widespread, mistaken belief that they provide simple, reliable, and objective triage tools for separating the true and important from the untrue or unimportant. The primary focus in interpreting therapeutic clinical research data should be on the treatment (“oomph”) effect, a metaphorical force that moves patients given an effective treatment to a different clinical state relative to their control counterparts. This effect is assessed using 2 complementary types of statistical measures calculated from the data, namely, effect magnitude or size and precision of the effect size. In a randomized trial, effect size is often summarized using constructs, such as odds ratios, hazard ratios, relative risks, or adverse event rate differences. How large a treatment effect has to be to be consequential is a matter for clinical judgment. The precision of the effect size (conceptually related to the amount of spread in the data) is usually addressed with confidence intervals. P values (significance tests) were first proposed as an informal heuristic to help assess how “unexpected” the observed effect size was if the true state of nature was no effect or no difference. Hypothesis testing was a modification of the significance test approach that envisioned controlling the false-positive rate of study results over many (hypothetical) repetitions of the experiment of interest. Both can be helpful but, by themselves, provide only a tunnel vision perspective on study results that ignores the clinical effects the study was conducted to measure.

Aggregate Uncertainty Can Lead to Incorrect Herds

A continuum of homogeneous rational agents choose between two competing technologies. Agents observe a private signal and sample others’ previous choices. Signals have an aggregate component of uncertainty, so aggregate behavior does not necessarily reflect the true state of nature. Nonetheless, agents still find others’ choices informative, and base their decisions partly on others’ behavior. Consequently, bad choices can be perpetuated: aggregate uncertainty makes agents herd on the inferior technology with positive probability. I derive the optimal decision rule when agents sample exactly two individuals. I also present examples with herds on the inferior technology for arbitrarily large sample sizes.

Social Learning With Bayesian Agents and Random Decision Making

In this paper, we study the effect of randomness in the decision making of agents on social learning. In the addressed system, the agents make decisions sequentially about the true state of nature. Each agent observes a signal produced according to one of two hypotheses, which represents the state of nature. The signals of all the agents are generated independently from the same state. The agents also know the decisions of all the previous agents in the network. The randomness in this paper is modeled by a policy that amounts to random mapping of the beliefs of the agents to the action space. We propose that the agents learn from the decisions of the previous agents and update their beliefs by using the Bayesian theory. We define the concept of social belief about the truthfulness of the two hypotheses and provide results on the convergence of the social belief. We also prove that with the proposed random policy, information cascade can be avoided and asymptotic learning occurs. We apply the random policy to data models that represent the observations by a distribution belonging to the exponential family. We then provide performance and convergence analysis of the proposed method as well as simulation results that include comparisons with deterministic and hybrid policies.

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.

The Value of Top-Down Communication for Organizational Performance

Economists have recently started to theoretically analyse the performance implications of transparency in organisations where the principal has private information. An important finding in this literature is that the principal may want to partly withhold information from the agent, and to expose him to uncertainty about the true state of nature. In this paper, we report empirical evidence that such uncertainty exposure is more detrimental to agent effort than previously thought. We design an experiment, in which an agent has only probabilistic beliefs about the true state of nature and needs to choose costly effort that benefits the principal. The true state relates to his fixed-wage, which can either be high or low. Prior to effort choice, the principal can create full transparency by disclosing the true state to the agent via costly communication. Our results show that principals should always create full transparency: even if this requires her to disclose 'bad news' (the low state), effort increases by 47 percent relative to non-disclosure. We show that the value of transparency extends beyond positive effects of the personal communication involved. However, half the principals misperceive this value and disclose information too restrictively, thereby foregoing 30 percent of their potential profits. To help overcome this misperception, we provide evidence on agents' various motivations to reward even the disclosure of bad news.

Structural Reliability of Spanish Softwoods. A New Answer for an Old Problem

A group of databases making a total of ca. 9.000 beams from coniferous trees from Spain, tested according EN 384 and visually graded according Spanish standards were analyzed. The main goal was to reach a detailed comprehension about the true state of nature of the three reference structural material properties (density, MOR and MOE), in the conditions it reaches the real market. Provided the existing beams (reasonably) came from the same populations, the use of reliability updating technics is a meaningful approach to the problem of conjecturing its resistant behavior. Distribution parameters for the target species have been proposed. Spanish softwood (with the exception of p. radiata) has proved to be extremely dense and quite stiff related to its resistance, compared with usual North American, Central or North European coniferous trees. Hence, the basic information of these last species should not be used when dealing with Spanish historical timbers, as it leads to rather uneconomical conjectures (as deformation and joints use to be design governing factors). Also therere significant differences related to the current Probabilistic Model Code suggestion on the topic (as the Code itself warns of).

A Simple Model of Repression and Revolt in Autocracies Applied to the Arab Spring

The aim of this paper is to provide a specific point of view on the protests that have characterized the Arab countries from December 2010 and are still going on. To understand some of the reasons behind these events, I propose a sequential game with asymmetric information on the likelihood of having a successful uprising. This means that on one hand the government choices may signal the true state of nature and on the other hand the citizens’ choices are a way to learn the truth. The model is simple and I assume specific parameters’ values to obtain a full characterization of the set of equilibria. This full characterization shows the interaction of three main factors in determining the possible resulting political regimes: the country’s wealth, the autocratic institutions and the general political mood. The complex interaction of these three variables explain why apparently heterogeneous countries may have similar political processes and apparently similar countries have different political situations.

A SIMPLE MODEL OF REPRESSION AND REVOLT IN AUTOCRACIES APPLIED TO THE ARAB SPRING

The aim of this paper is to provide a specific point of view on the protests that have characterized the Arab countries from December 2010 and are still going on. To understand some of the reasons behind these events, I propose a sequential game with asymmetric information on the likelihood of having a successful uprising. This means that on one hand the government choices may signal the true state of nature and on the other hand the citizens' choices are a way to learn the truth. I assume specific parameters' values to obtain a full characterization of the set of equilibria. This allows to show the interaction of three main factors in determining the possible resulting political regimes: the country's wealth, the autocratic institutions and the general political mood. The complex interaction of these three variables explains why apparently heterogeneous countries may have similar political processes and apparently similar countries face different political situations.

Robust Incentives for Information Acquisition

A principal needs to make a decision. An expert is able to supply information that is helpful to the principal, but only by exerting effort, which is costly. The principal can incentivize the expert to put in effort by paying him a reward based on his reported information and on the true state of nature, which is revealed ex post. There is limited liability - payments can never be negative. The principal is uncertain about the expert's information acquisition technology: she knows some actions (experiments) that he can take to obtain information, but there may also be other experiments available. In the face of this uncertainty, the principal evaluates any incentive contract she may offer using a maxmin expected utility criterion. Under quite general conditions, we show that the optimal contract is a restricted investment contract, in which the expert chooses from a subset of the decisions available to the principal, and is then rewarded proportionally to the value of his designated decision in the realized state (although the decision that the principal actually makes need not be the same as the one designated by the expert).

Error types in reservoir characterization and management

This paper discusses types of errors in reservoir characterization and management. Due to intrinsic relationships between error and uncertainty, analysis of error types can help mitigate uncertainty in resource evaluation. First, measurements may contain errors or have a range of uncertainty. Second, because measurements typically represent only a small sampling fraction of a reservoir, description or modeling of the entire reservoir based on the limited measurements involves uncertainty and prediction errors. Two error classification methods are introduced for natural resource characterization. The first proposed method distinguishes between data errors and inference errors, each of which is further divided into subtypes. The second method classifies two types of errors based on the mismatch between one's interpretation and the true state of nature, which can provide efficient feedback to validate or refute the method used in a reservoir characterization. This method is further extended to analyze composite errors in hydrocarbon resource evaluation and reservoir management.

Bank failures caused by Large withdrawals: An explanation based purely on liquidity

In a version of the Diamond and Dybvig [Diamond, D., Dybvig, P., 1983. Bank runs, deposit insurance, and liquidity. Journal of Political Economy 91, 401–419.] model with aggregate uncertainty, we show that there exists an equilibrium with the following properties: all consumers deposit at the bank, all patient consumers wait for the last period to withdraw, and the bank fails with strictly positive probability. Furthermore, we show that the probability of a bank failure remains bounded away from zero as the number of consumers increases. This equilibrium explains bank failures driven by extreme withdrawals solely on liquidity since they happen because both banks and depositors are illiquid. Furthermore, it does not require much of the elements typically emphasized, including: consumers well informed about the true state of nature, a non-zero consumption after a crisis, consumers’ panic and sunspots. We therefore think that aggregate risk in Diamond-Dybvig-like environments can be an important element to explain bank crises.

Costly State Verification with Varying Risk Preferences and Liability

Abstract. In the scenario of loan contracts with costly state verification, we examine how the properties of the set of states, different risk preferences of debtors and varying liability of lenders affect the structure of optimal repayments. In particular, we show that with risk-averse debtors, a general set of states, a constant observation cost and both unlimited and limited lender liability, the debtor is strictly better off revealing the true state of nature when his realized revenue is low, which implies that optimal debtor consumption has a downward jump around the single switch from observed to unobserved states. If the debtor can destroy revenue or if the debtor is risk neutral, this non-monotonicity of consumption disappears. Moreover, given the loan size, there is more monitoring under debtor-risk aversion than risk neutrality. We present simulations showing that a contract with unlimited lender liability and debtor-risk aversion has a higher expected observation cost but a lower variance of consumption than a contract with limited lender liability. Finally, we discuss the problems of commitment to verification and contract renegotiation in this framework.

A contest with the taxman – the impact of tax rates on tax evasion and wastefully invested resources

We develop a moral hazard model with auditing where both the principal and the agent can influence the probability that the true state of nature is verified. This setting is widely applicable for situations where fraudulent reporting with costly state verification takes place. However, we use the framework to investigate tax evasion. We model tax evasion as a concealment-detection contest between the taxpayer and the authority. We show that higher tax rates cause more evasion and increase the resources wasted in the contest. Additionally, we find conditions under which a government should enforce incentive compatible auditing in order to reduce wasted resources.

On the Existence of Equilibrium Bank Runs in a Diamond-Dybvig Environment

In a version of the Diamond and Dybvig [6] model with aggregate uncertainty, we show that there exists an equilibrium with the following properties: all consumers deposit at the bank, all patient consumers wait for the last period to withdraw, and the bank fails with strictly positive probability. Furthermore, we show that the probability of a bank failure remains bounded away from zero as the number of consumers increases. We interpret such an equilibrium as reflecting a bank run, defined as an episode in which a large number of people withdraw their deposits from a bank, forcing it to fail. Our results show that we can have equilibrium bank runs with consumers poorly informed about the true state of nature, a sequential service constraint, an infinite marginal utility of consumption at zero, and without consumers' panic and sunspots. We therefore think that aggregate risk in Diamond-Dybvig-like environments can be an important element to explain bank runs.