Experimental Economics: Contributions, Recent Developments, and New Challenges

Although economics has long been considered as a non-experimental science, the development of experimental economics and behavioral economics is amazingly rapid and affects most fields of research. This paper first attempts at defining the main contributions of experiments to economics. It also identifies four main trends in the development of experimental research in economics. The third contribution of this paper is to identify the major theoretical and methodological challenges faced by behavioral and experimental economics.behavioral economy ; Experimental economics ; field experiment ; quantitative methods

Experimental Economics: Contributions, Recent Developments, and New Challenges

Although economics has long been considered as a non-experimental science, the development of experimental economics and behavioral economics is amazingly rapid and affects most fields of research. This paper first attempts at defining the main contributions of experiments to economics. It also identifies four main trends in the development of experimental research in economics. The third contribution of this paper is to identify the major theoretical and methodological challenges faced by behavioral and experimental economics.experimental economics; neuroeconomics; quantitative methods; field experiments

Human centric situational awareness

Context awareness is an approach that has been receiving increasing focus in the past years. A context aware device can understand surrounding conditions and adapt its behavior accordingly to meet user demands. Mobile handheld devices offer a motivating platform for context aware applications as a result of their rapidly growing set of features and sensing abilities. This research aims at building a situational awareness model that utilizes multimodal sensor data provided through the various sensing capabilities available on a wide range of current handheld smart phones. The model will make use of seven different virtual and physical sensors commonly available on mobile devices, to gather a large set of parameters that identify the occurrence of a situation for one of five predefined context scenarios: In meeting, Driving, in party, In Theatre and Sleeping. As means of gathering the wisdom of the crowd and in an effort to reach a habitat sensitive awareness model, a survey was conducted to understand the user perception of each context situation. The data collected was used to build the inference engine of a prototype context awareness system utilizing context weights introduced in [39] and the confidence metric in [26] with some variation as a means for reasoning. The developed prototype\u27s results were benchmarked against two existing context awareness platforms Darwin Phones [17] and Smart Profile [11], where the prototype was able to acquire 5% and 7.6% higher accuracy levels than the two systems respectively while performing tasks of higher complexity. The detailed results and evaluation are highlighted further in section 6.4

Expertise in unexpected places: selective social learning from counter-normative experts

Previous research demonstrates that children prefer to use information given by people of their own gender when learning about their environment. However, young children are also very sensitive to the specialized knowledge, or expertise, of others. The present work explored whether children are willing to learn from an expert informant who displays non - traditional gender role interests. Four- to 8-year-olds were presented with conflicting opinions about a piece of domain specific information from a counter-stereotypical expert (e.g., a boy with expertise in ballet), as well as a layperson of the opposite gender (e.g., a girl with little knowledge about ballet). Participants were asked to choose who they believed was correct, who they would prefer to learn from in the future, and how much they liked each character. Overall, participants selected the counter-stereotypical expert as correct. However, 4- to 5-year-olds reported a preference to learn from same-gender participants in the future irrespective of their expertise, whereas 6- to 8-year-olds reported wanting to learn from the counter-stereotypical expert in the future. Gender differences also emerged, with boys of all ages showing greater acceptance of the opinion of a male counter-stereotypical expert as compared to a female counter-stereotypical expert. These results demonstrate that while expertise is a powerful learning cue, there are circumstances in which expert testimony may be disregarded in favor of potent social categorical biases

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Building expert systems: cognitive emulation.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis

INFERENCE-BASED FORENSICS FOR EXTRACTING INFORMATION FROM DIVERSE SOURCES

Digital forensics is tasked with the examination and extraction of evidence from a diverse set of devices and information sources. While digital forensics has long been synonymous with file recovery, this label no longer adequately describes the science’s role in modern investigations. Spurred by evolving technologies and online crime, law enforcement is shifting the focus of digital forensics from its traditional role in the final stages of an investigation to assisting investigators in the earliest phases — often before a suspect has been identified and a warrant served. Investigators need new forensic techniques to investigate online crimes, such as child pornography trafficking on peer-to-peer networks (p2p), and to extract evidence from new information sources, such as mobile phones. The traditional approach of developing tools tailored specifically to each source is no longer tenable given the diversity, volume of storage, and introduction rate of new devices and network applications. Instead, we propose the adoption of flexible, inference-based techniques to extract evidence from any format. Such techniques can be readily applied to a wide variety of different evidence sources without requiring significant manual work on the investigator’s part. The primary contribution of my dissertation is a set of novel forensic techniques for extracting information from diverse data sources. We frame the evaluation using two different, but increasingly important, forensic scenarios: mobile phone triage and network-based investigations. Via probabilistic descriptions of typical data structures, and using a classic dynamic programming algorithm, our phone triage techniques are able to identify user information in phones across varied models and manufacturers. We also show how to incorporate feedback from the investigator to improve the usability of extracted information. For network-based investigations, we quantify and characterize the extent of contraband trafficking on peer-to-peer networks. We suggest various techniques for prioritizing law enforcement’s limited resources. We finally investigate techniques that use system logs to generate and then analyze a finite state model of a protocol’s implementation. The objective is to infer behavior that an investigator can leverage to further law enforcement objectives. We evaluate all of our techniques using the real-world legal constraints and restrictions of investigators

Lab Labor: What Can Labor Economists Learn from the Lab?

This paper surveys the contributions of laboratory experiments to labor economics. We begin with a discussion of methodological issues: why (and when) is a lab experiment the best approach; how do laboratory experiments compare to field experiments; and what are the main design issues? We then summarize the substantive contributions of laboratory experiments to our understanding of principal-agent interactions, social preferences, union-firm bargaining, arbitration, gender differentials, discrimination, job search, and labor markets more generally.personnel economics, principal-agent theory, laboratory experiments, labor economics