A Conceptual Framework for Investigating Organizational Control and Resistance in Crowd-Based Platforms

This paper presents a research agenda for crowd behavior research by drawing from the organizational control literature. It addresses the need for research into the organizational and social structures that guide user behavior and contributions in crowd-based platforms. Crowd behavior is situated within a conceptual framework of organizational control. This framework helps scholars more fully articulate the full range of control mechanisms operating in crowd-based platforms, contextualizes these mechanisms into the context of crowd-based platforms, challenges existing rational assumptions about incentive systems, and clarifies theoretical constructs of organizational control to foster stronger integration between information systems research and organizational and management science

Essays In Algorithmic Market Design Under Social Constraints

Rapid technological advances over the past few decades---in particular, the rise of the internet---has significantly reshaped and expanded the meaning of our everyday social activities, including our interactions with our social circle, the media, and our political and economic activities This dissertation aims to tackle some of the unique societal challenges underlying the design of automated online platforms that interact with people and organizations---namely, those imposed by legal, ethical, and strategic considerations. I narrow down attention to fairness considerations, learning with repeated trials, and competition for market share. In each case, I investigate the broad issue in a particular context (i.e. online market), and present the solution my research offers to the problem in that application. Addressing interdisciplinary problems, such as the ones in this dissertation, requires drawing ideas and techniques from various disciplines, including theoretical computer science, microeconomics, and applied statistics. The research presented here utilizes a combination of theoretical and data analysis tools to shed light on some of the key challenges in designing algorithms for today\u27s online markets, including crowdsourcing and labor markets, online advertising, and social networks among others

Agent based simulation of workers' behaviours around hazard areas in manufacturing sites

Rewards for risk taking behaviour by workers (if accidents do not occur) can be realised in the form of increased productivity or worker idle time. However, frequent unsafe behaviours of workers inevitably results in accidents and an associated loss in productivity. Workers' behaviour towards safety is influenced by management, who can encourage or discourage risk taking behaviour. In this paper, we explore the relationship between the perceived reward by individual workers who expose themselves to hazards and a management response in the form of inspections to monitor and address inappropriate behaviours. We conduct this study by developing an Agent Based Simulation Model, where workers are required to learn paths within a factory exposed to hazardous areas, with inspectors randomly moving around the factory to correct inappropriate behaviour if noticed. We assume workers are maximising their anticipated reward as they learn routes through the factory. This agent based model is used to explore the impact of inspection frequency and reward perception (i.e. parameters which can be influenced by management) on the number of workplace accident. The results demonstrated that the proposed model is a valuable tool to assist the management in predicting the potential safety improvement from safety management practices focusing on safety inspections, and changing workers perceptions

A Multi-Dimensional Approach for Framing Crowdsourcing Archetypes

All different kinds of organizations – business, public, and non-governmental alike – are becoming aware of a soaring complexity in problem solving, decision making and idea development. In a multitude of circumstances, multidisciplinary teams, high-caliber skilled resources and world-class computer suites do not suffice to cope with such a complexity: in fact, a further need concerns the sharing and ‘externalization’ of tacit knowledge already existing in the society. In this direction, participatory tendencies flourishing in the interconnected society in which we live today lead ‘collective intelligence’ to emerge as key ingredient of distributed problem solving systems going well beyond the traditional boundaries of organizations. Resulting outputs can remarkably enrich decision processes and creative processes carried out by indoor experts, allowing organizations to reap benefits in terms of opportunity, time and cost. Taking stock of the mare magnum of promising opportunities to be tapped, of the inherent diversity lying among them, and of the enormous success of some initiative launched hitherto, the thesis aspires to provide a sound basis for the clear comprehension and systematic exploitation of crowdsourcing. After a thorough literature review, the thesis explores new ways for formalizing crowdsourcing models with the aim of distilling a brand-new multi-dimensional framework to categorize various crowdsourcing archetypes. To say it in a nutshell, the proposed framework combines two dimensions (i.e., motivations to participate and organization of external solvers) in order to portray six archetypes. Among the numerous significant elements of novelty brought by this framework, the prominent one is the ‘holistic’ approach that combines both profit and non-profit, trying to put private and public sectors under a common roof in order to examine in a whole corpus the multi-faceted mechanisms for mobilizing and harnessing competence and expertise which are distributed among the crowd. Looking at how the crowd may be turned into value to be internalized by organizations, the thesis examines crowdsourcing practices in the public as well in the private sector. Regarding the former, the investigation leverages the experience into the PADGETS project through action research – drawing on theoretical studies as well as on intensive fieldwork activities – to systematize how crowdsourcing can be fruitfully incorporated into the policy lifecycle. Concerning the private realm, a cohort of real cases in the limelight is examined – having recourse to case study methodology – to formalize different ways through which crowdsourcing becomes a business model game-changer. Finally, the two perspectives (i.e., public and private) are coalesced into an integrated view acting as a backdrop for proposing next-generation governance model massively hinged on crowdsourcing. In fact, drawing on archetypes schematized, the thesis depicts a potential paradigm that government may embrace in the coming future to tap the potential of collective intelligence, thus maximizing the utilization of a resource that today seems certainly underexploited

Achieving reliability and fairness in online task computing environments

Mención Internacional en el título de doctorWe consider online task computing environments such as volunteer computing platforms running on BOINC (e.g., SETI@home) and crowdsourcing platforms such as Amazon Mechanical Turk. We model the computations as an Internet-based task computing system under the masterworker paradigm. A master entity sends tasks across the Internet, to worker entities willing to perform a computational task. Workers execute the tasks, and report back the results, completing the computational round. Unfortunately, workers are untrustworthy and might report an incorrect result. Thus, the first research question we answer in this work is how to design a reliable masterworker task computing system. We capture the workers’ behavior through two realistic models: (1) the “error probability model” which assumes the presence of altruistic workers willing to provide correct results and the presence of troll workers aiming at providing random incorrect results. Both types of workers suffer from an error probability altering their intended response. (2) The “rationality model” which assumes the presence of altruistic workers, always reporting a correct result, the presence of malicious workers always reporting an incorrect result, and the presence of rational workers following a strategy that will maximize their utility (benefit). The rational workers can choose among two strategies: either be honest and report a correct result, or cheat and report an incorrect result. Our two modeling assumptions on the workers’ behavior are supported by an experimental evaluation we have performed on Amazon Mechanical Turk. Given the error probability model, we evaluate two reliability techniques: (1) “voting” and (2) “auditing” in terms of task assignments required and time invested for computing correctly a set of tasks with high probability. Considering the rationality model, we take an evolutionary game theoretic approach and we design mechanisms that eventually achieve a reliable computational platform where the master receives the correct task result with probability one and with minimal auditing cost. The designed mechanisms provide incentives to the rational workers, reinforcing their strategy to a correct behavior, while they are complemented by four reputation schemes that cope with malice. Finally, we also design a mechanism that deals with unresponsive workers by keeping a reputation related to the workers’ response rate. The designed mechanism selects the most reliable and active workers in each computational round. Simulations, among other, depict the trade-off between the master’s cost and the time the system needs to reach a state where the master always receives the correct task result. The second research question we answer in this work concerns the fair and efficient distribution of workers among the masters over multiple computational rounds. Masters with similar tasks are competing for the same set of workers at each computational round. Workers must be assigned to the masters in a fair manner; when the master values a worker’s contribution the most. We consider that a master might have a strategic behavior, declaring a dishonest valuation on a worker in each round, in an attempt to increase its benefit. This strategic behavior from the side of the masters might lead to unfair and inefficient assignments of workers. Applying renown auction mechanisms to solve the problem at hand can be infeasible since monetary payments are required on the side of the masters. Hence, we present an alternative mechanism for fair and efficient distribution of the workers in the presence of strategic masters, without the use of monetary incentives. We show analytically that our designed mechanism guarantees fairness, is socially efficient, and is truthful. Simulations favourably compare our designed mechanism with two benchmark auction mechanisms.This work has been supported by IMDEA Networks Institute and the Spanish Ministry of Education grant FPU2013-03792.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Alberto Tarable.- Secretario: José Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo Guervó

EXAMINING THE UTILITY OF BEHAVIORAL ECONOMIC DEMAND IN ADDICTION SCIENCE

The marriage of perspectives from behavioral economic theory and learning theory has the potential to advance an understanding of substance use and substance use disorder. Behavioral economic demand is a central concept to this interdisciplinary approach. Evaluating demand in the laboratory and clinic can improve previous research on the relative reinforcing effects of drugs by accounting for the multi-dimensional nature of reinforcement rather than viewing reinforcement as a unitary construct. Recent advances in the commodity purchase task methodology have further simplified the measurement of demand values in human participants. This dissertation project presents a programmatic series of studies designed to demonstrate the utility of using a behavioral economic demand framework and the purchase task methodology for understanding substance use disorder through basic and applied science research. Experiments are presented spanning a continuum from theoretical and methodological development to longitudinal work and clinical application. These experiments demonstrate three key conclusions regarding behavioral economic demand. First, behavioral economic demand provides a reliable and valid measure of drug valuation that is applicable to varied drug types and participant populations. Second, behavioral economic demand is a stimulus-selective measure specifically reflecting valuation for the commodity under study. Third, behavioral economic demand provides incremental information about substance use in the laboratory and clinical setting above and beyond traditional measures of reinforcer valuation and other behavioral economic variables. These findings collectively highlight the benefits of behavioral economic demand and provide an important platform for future work in addiction science

A Study of Ethics in Crowd Work-Based Research

Crowd work as a form of a social-technical system has become a popular setting for conducting and distributing academic research. Crowd work platforms such as Amazon Mechanical Turk (MTurk) are widely used by academic researchers. Recent scholarship has highlighted the importance of ethical issues because they could affect the long-term development and application of crowd work in various fields such as the gig economy. However, little study or deliberation has been conducted on the ethical issues associated with academic research in this context. Current sources for ethical research practice, such as the Belmont Report, have not been examined thoroughly on how they should be applied to tackle the ethical issues in crowd work-based research such as those in data collection and usage. Hence, how crowd work-based research should be conducted to make it respectful, beneficent, and just is still an open question. This dissertation research has pursued this open question by interviewing 15 academic researchers and 17 IRB directors and analysts in terms of their perceptions and reflections on ethics in research on MTurk; meanwhile, it has analyzed 15 research guidelines and consent templates for research on MTurk and 14 published papers from the interviewed scholars. Based on analyzing these different sources of data, this dissertation research has identified three dimensions of ethics in crowd work-based research, including ethical issues in payment, data, and human subjects. This dissertation research also uncovered the “original sin” of these ethical issues and discussed its impact in academia, as well as the limitations of the Belmont Report and AoIR Ethical Guidelines 3.0 for Internet Research. The findings and implications of this research can help researchers and IRBs be more conscious about ethics in crowd work-based research and also inspire academic associations such as AoIR to develop ethical guidelines that can address these ethical issues