scholarly journals Tractable (Simple) Contests

2018 ◽  
Author(s):  
Priel Levy ◽  
David Sarne ◽  
Yonatan Aumann
Keyword(s):  
Theoretical Analysis ◽  
Design Optimization ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Behavior Prediction ◽  
Optimal Parameters ◽  
Equilibrium Behavior ◽  
Multi Agent ◽  
Contest Design ◽  
Pure Strategies

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.

scholarly journals Evaluating extinction, renewal, and resurgence of operant behavior in humans with Amazon Mechanical Turk

2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik
Keyword(s):  
Operant Behavior ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk

scholarly journals Amazon Mechanical Turk: Gold Mine or Coal Mine?

2011 ◽  
Vol 37 (2) ◽  
pp. 413-420 ◽  
Author(s):  
Karën Fort ◽  
Gilles Adda ◽  
K. Bretonnel Cohen
Keyword(s):  
Coal Mine ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Gold Mine

scholarly journals Developing and validating an isotrigon texture discrimination task using Amazon Mechanical Turk

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
John WG Seamons ◽  
Marconi S Barbosa ◽  
Jonathan D Victor ◽  
Dominique Coy ◽  
Ted Maddess
Keyword(s):  
Discrimination Task ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Texture Discrimination

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

Consumer perception of portable ladder hazards and warnings

2018 ◽  
Vol 62 (1) ◽  
pp. 411-415
Author(s):  
Kenneth Nemire
Keyword(s):  
Significant Relationship ◽  
Online Survey ◽  
Consumer Perceptions ◽  
Consumer Perception ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Future Research ◽  
Warning Labels ◽  
Preliminary Assessment ◽  
Survey Tool

This article describes the results of a survey intended as a preliminary assessment of consumer perceptions of the hazardousness of portable ladders and the warning labels provided on portable ladders. One hundred ten participants responded to an online survey tool called Amazon Mechanical Turk. The survey collected information about participants’ use of ladders, their ratings of familiarity with ladders, perceived hazardousness of portable ladders, and perception of warning labels on portable ladders. Results indicated a small but significant relationship between familiarity with ladders and their perceived hazardousness, and that participants thought that people should be warned about the hazards associated with ladder use. Implications for future research about consumer perception of portable ladder hazards and warnings are described.

A Primer for Conducting Survey Research using MTurk

2016 ◽  
Vol 7 (2) ◽  
pp. 54-73 ◽  
Author(s):  
Silvana Chambers ◽  
Kim Nimon ◽  
Paula Anthony-McMann
Keyword(s):  
Best Practices ◽  
Survey Research ◽  
Survey Design ◽  
Design Guidelines ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Sample Survey ◽  
Quality Data ◽  
Trade Offs

This paper presents best practices for conducting survey research using Amazon Mechanical Turk (MTurk). Readers will learn the benefits, limitations, and trade-offs of using MTurk as compared to other recruitment services, including SurveyMonkey and Qualtrics. A synthesis of survey design guidelines along with a sample survey are presented to help researchers collect the best quality data. Techniques, including SPSS and R syntax, are provided that demonstrate how users can clean resulting data and identify valid responses for which workers could be paid.

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