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In observational data, access to information is associated with lower levels of corruption. This chapter reviews a small but growing body of work that uses field experiments to explore the mechanisms behind this relationship. We present a typology for understanding this research based on the type of corruption being addressed (political vs. bureaucratic), the mechanism for accountability (retrospective vs. prospective), and the nature of the information provided (factual vs. prescriptive). We describe some of the tradeoffs involved in design decisions for such experiments and suggest directions for future research.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

This paper aims to investigate the effect of cancer patients’ information behaviour on their decision-making at the diagnosis and treatment stages of their cancer journey. Patients’ information sources and their decision-making approaches were analyzed.

Semi-structured interviews were conducted with 15 participants.

The cancer patients sought information from various sources in choosing a hospital, physician, treatment method, diet and alternative therapy. Physicians were the primary information source. The patients’ approaches to treatment decision-making were diverse. An informed approach was adopted by nine patients, a paternalistic approach by four and a shared decision-making approach by only two.

In practice, the findings may assist hospitals and medical professionals in fostering pertinent interactions with patients.

The findings can enhance researcher understanding regarding the effect of cancer patients’ information behaviour on their decision-making.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

This article explores the importance of accessible and focal information in influencing beliefs and attention in a learning-to-forecast laboratory experiment where subjects are incentivized to form accurate expectations about inflation and the output gap. We consider the effects of salient and accessible forecast error information and learning on subjects’ forecasting accuracy and heuristics, and on aggregate stability. Experimental evidence indicates that, while there is considerable heterogeneity in the heuristics used, subjects’ forecasts can be best described by a constant gain learning model where subjects respond to forecast errors. Salient forecast error information reduces subjects’ overreaction to their errors and leads to greater forecast accuracy, coordination of expectations, and macroeconomic stability. The benefits of this focal information are short-lived and diminish with learning.

We investigate whether information technology (IT) can help physicians more efficiently acquire new knowledge in a clinical environment characterized by information overload. We combine analysis of data from a randomized trial with a theoretical model of the influence that IT has on the acquisition of new medical knowledge. Although the theoretical framework we develop is conventionally microeconomic, the model highlights the non-market and non-pecuniary influence activities that have been emphasized in the sociological literature on technology diffusion. We report three findings. First, empirical evidence and theoretical reasoning suggests that computer-based decision support will speed the diffusion of new medical knowledge when physicians are coping with information overload. Second, spillover effects will likely lead to “underinvestment” in this decision support technology. Third, alternative financing strategies common to new IT, such as the use of marketing dollars to pay for the decision support systems, may lead to undesirable outcomes if physician information overload is sufficiently severe and if there is significant ambiguity in how best to respond to the clinical issues identified by the computer. This is the first paper to analyze empirically and theoretically how computer-based decision support influences the acquisition of new knowledge by physicians.

Previous studies showed mixed results as to the cause of myopic loss aversion (MLA). This paper reexamines the main driver of MLA, considering two factors from previous studies and an additional factor.

Experimentally investigate whether flexibility of investment, frequency of information feedback, or timing of decision cause MLA.

Timing of decision and flexibility of investment explain most differences in subject behavior. Frequency of information feedback makes only a marginal contribution.

The differences in subject behavior can be interpreted by a shift in their reference points depending on the difference in flexibility of investment, frequency of information feedback, or timing of decision.

The purpose of this chapter is to study opposition of the treatment of the role and meaning of information in an economic system in the conditions of post-industrial and information economy and develop the conceptual model of information economy through the prism of treatment of information.

The authors use the method of comparative and systemic analysis, as well as synthesis, induction, deduction, formalization, and modeling of economic phenomena and processes.

The authors conducted a comparative analysis of the role and meaning of information in the economic system in a post-industrial information economy. It is concluded that the most important qualitative characteristic of information economy, which distinguished it from another, is the key role of information. The proprietary conceptual model of an information economy through the prism of information treatment is offered – it reflects the top-priority role of sustainable socio-economic and managerial connections and relations of information, which are an essential qualitative peculiarity of an information economy, which separate it and determine its specifics as compared to other types of economic systems.

It is substantiated that an information economy has to be studied not only from the position of economic statistics on the basis of precise values of quantitative indicators but also from the position of the economic theory of behaviorism, which allows taking into account the relational aspects of functioning and development of information economy. Behavior of economic subjects (society and business) as to information, initiated by their own motives, has to be one of the key objects of study by the theory of information economy.

Methodological recommendations are developed, which allow – with high level of effectiveness – studying information economy from the positions of economic statistics and the economic theory of behaviorism.

Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects’ behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also identify some “black spots” where the theory does least well in interpreting the data.