632 research outputs found
Data Mining in Electronic Commerce
Modern business is rushing toward e-commerce. If the transition is done
properly, it enables better management, new services, lower transaction costs
and better customer relations. Success depends on skilled information
technologists, among whom are statisticians. This paper focuses on some of the
contributions that statisticians are making to help change the business world,
especially through the development and application of data mining methods. This
is a very large area, and the topics we cover are chosen to avoid overlap with
other papers in this special issue, as well as to respect the limitations of
our expertise. Inevitably, electronic commerce has raised and is raising fresh
research problems in a very wide range of statistical areas, and we try to
emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Crowdsourcing Contests: A Dynamic Structural Model of the Impact of Incentive Structure on Solution Quality
A key challenge faced by firms that undertake crowdsourcing-contests to get solutions from crowds to their problems is to design an incentive structure which helps attract high quality solutions. We develop a structural model of user participation in crowdsourcing-contests and present empirical evidence on how incentive structure could affect the quality of solutions. Using data from Threadless.com, we find that participants exert less effort as competition for the reward increases. This may indicate that increasing the reward may adversely affect the quality of the solutions produced as it will increase the competition. However, counter-intuitively the policy simulations indicate that increasing the reward increases both quantity and quality of the solutions. This is because under the new policy of higher reward, individual equilibrium behavior is different. When the firm increases the reward, the additional utility from increase in the reward offsets the reduction in probability of winning due to intensified competition
Intention To Disclose Personal Information Via Mobile Applications: A Privacy Calculus Perspective
This study aimed to investigate the issue of consumer intention to disclose personal information via mobile applications (apps). Drawing on the literature of privacy calculus theory, this research examined the factors that influence the trade-off decision of receiving perceived benefits and being penalized with perceived risks through the calculus lens. In particular, two paths of the direct effects on perceived benefits and risks that induce the ultimate intention to disclose personal information via mobile apps were proposed and empirically tested. The analysis showed that self-presentation and personalized services positively influence consumers’ perceived benefits, which in turn positively affects the intention to dis- close personal information. Perceived severity and perceived control serve as the direct antecedents of perceived risks that negatively affect the intention of consumers to disclose personal information. Compared with the perceived risks, the perceived benefits more strongly influence the intention to disclose personal information. This study extends the literature on privacy concerns to consumer intention to disclose personal information by theoretically developing and empirically testing four hypotheses in a research model. Results were validated in the mobile context, and implications and discussions were presented
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Electronic commerce is revolutionizing the way we think about
data modeling, by making it possible to integrate the processes of
(costly) data acquisition and model induction. The opportunity for
improving modeling through costly data acquisition presents itself
for a diverse set of electronic commerce modeling tasks, from personalization
to customer lifetime value modeling; we illustrate with
the running example of choosing offers to display to web-site visitors,
which captures important aspects in a familiar setting. Considering
data acquisition costs explicitly can allow the building of
predictive models at significantly lower costs, and a modeler may
be able to improve performance via new sources of information that
previously were too expensive to consider. However, existing techniques
for integrating modeling and data acquisition cannot deal
with the rich environment that electronic commerce presents. We
discuss several possible data acquisition settings, the challenges involved
in the integration with modeling, and various research areas
that may supply parts of an ultimate solution. We also present and
demonstrate briefly a unified framework within which one can integrate
acquisitions of different types, with any cost structure and
any predictive modeling objectiveNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
The Influence of Social Norms and Social Consciousness on Intention Reconciliation
Research on resource-bounded agents has established that rational agents need to be able to revise their commitments in light of new opportunities. In the context of collaborative activities, rational agents must be able to reconcile their intentions to do team-related actions with other, conflicting intentions. The SPIRE experimental system allows the process of intention reconciliation in team contexts to be simulated and studied. Initial work with SPIRE examined the impact of environmental factors and agent utility functions on individual and group outcomes in the context of one set of social norms governing collaboration. This paper extends those results by further studying the effect of environmental factors and the agents' level of social consciousness and by comparing the impact of two different types of social norms on agent behavior and outcomes. The results show that the choice of social norms influences the accuracy of the agents' responses to varying environmental factors, as well as the effectiveness of social consciousness and other aspects of agents' utility functions. In experiments using heterogeneous groups of agents, both sets of norms were susceptible to the free-rider effect. However, the gains of the less responsible agents were minimal, suggesting that agent designers would have little incentive to design agents that deviate from the standard level of responsibility to the group.Engineering and Applied Science
Achieving reliability and fairness in online task computing environments
MenciĂłn Internacional en el tĂtulo 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 IngenierĂa MatemáticaPresidente: Alberto Tarable.- Secretario: JosĂ© Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo GuervĂł
Optimizing the Performance of Robotic Mobile Fulfillment Systems
A robotic mobile fulfillment system is a novel type of automated part-to-picker material handling system. In this type of system, robots transport mobile shelves, called pods, containing items between the storage area and the workstations. It is well suited to e-commerce, due to its modularity and it's ability to adapt to changing orders patterns. Robots can nearly instantaneously switch between inbound and outbound tasks, pods can be continually repositioned to allow for automatic sorting of the inventory, pods can contain many different types of items, and unloaded robots can drive underneath pods, allowing them to use completely different routes than loaded robots.
This thesis studies the performance of robotic mobile fulfillment systems by solving decision problems related to warehouse design, inventory and resource allocation, and real-time operations. For warehouse design, a new queueing network is developed that incorporates realistic robot movement, storage zones, and multi-line orders. For inventory allocation, we develop a new type of queueing network, the cross-class matching multi-class semi-open queueing network, which can be applied to other systems as well. Resource (re)allocation is modeled by combining queueing networks with Markov decision processes while including time-varying demand. This model compares benchmark policies from practice wit
Mergers and acquisitions transactions strategies in diffusion - type financial systems in highly volatile global capital markets with nonlinearities
The M and A transactions represent a wide range of unique business
optimization opportunities in the corporate transformation deals, which are
usually characterized by the high level of total risk. The M and A transactions
can be successfully implemented by taking to an account the size of
investments, purchase price, direction of transaction, type of transaction, and
using the modern comparable transactions analysis and the business valuation
techniques in the diffusion type financial systems in the finances. We
developed the MicroMA software program with the embedded optimized
near-real-time artificial intelligence algorithm to create the winning virtuous
M and A strategies, using the financial performance characteristics of the
involved firms, and to estimate the probability of the M and A transaction
completion success. We believe that the fluctuating dependence of M and A
transactions number over the certain time period is quasi periodic. We think
that there are many factors, which can generate the quasi periodic oscillations
of the M and A transactions number in the time domain, for example: the stock
market bubble effects. We performed the research of the nonlinearities in the M
and A transactions number quasi-periodic oscillations in Matlab, including the
ideal, linear, quadratic, and exponential dependences. We discovered that the
average of a sum of random numbers in the M and A transactions time series
represents a time series with the quasi periodic systematic oscillations, which
can be finely approximated by the polynomial numbers. We think that, in the
course of the M and A transaction implementation, the ability by the companies
to absorb the newly acquired knowledge and to create the new innovative
knowledge bases, is a key predeterminant of the M and A deal completion success
as in Switzerland.Comment: 160 pages, 9 figures, 37 table
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