Doodle: an innovative tool for organizing group tutorials in University education

[EN] Group tutorials are becoming an ever-increasing learning methodology in University education due to the continuous knowledge feedback among students. Despite the positive impact of such kind of sessions on students, their previous organization phase remains most of the times misleading. The traditional way of arranging a group tutorial through e-mail normally results in a long and ineffective method mainly caused by the different schedule availability between the professor and the different students. Is in this context where Doodle arises as a virtual application to enhance this first group tutorial phase. Generally, Doodle allows users to schedule meetings in a quick, effective and free way: the organizer creates a new meeting, proposes different schedule options and invites the other participants through an e-mail invitation or a link created by Doodle. Then, participants vote for the schedule options that best fit their availability, so that the final meeting schedule is selected democratically. In the University context, professors would play the role of organizers and students of participants, respectively. In this paper, we analyze the application of Doodle in the organization of a group tutorial of students of Electrical Circuits from the Bachelor Degree in Electrical Engineering (Polytechnic University of Valencia). Particularly, the tutorial was formed by six students and the professor, and took place in the Department of Electrical Engineering. After the meeting, the students answered a survey. Their answers reveled the positive acceptance of Doodle among them in terms of efficiency and ease of use. 100% of them agree on its suitability for arranging future group tutorials. Moreover, a comparative study demonstrated that using Doodle instead of e-mail while arranging a group tutorial leads to an average of up to 64% reduction in process time.This work was supported in part by the regional public administration of Valencia under the grant ACIF/2018/106.Bastida Molina, P.; Vargas Salgado, CA.; Montuori, L.; Alcázar Ortega, M. (2021). Doodle: an innovative tool for organizing group tutorials in University education. En Proceedings INNODOCT/20. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 185-193. https://doi.org/10.4995/INN2020.2020.11883OCS18519

Bots as Virtual Confederates: Design and Ethics

The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool comes with inherent ethical challenges. Informed consent can be difficult to obtain in many cases, and the use of confederates necessarily implies the use of deception. In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation. We draw upon examples from prior work in the CSCW community and the broader social science literature for illustration. While a handful of prior researchers have used bots in online experimentation, our work is meant to inspire future work in this area and raise awareness of the associated ethical issues.Comment: Forthcoming in CSCW 201

Heuristics in Multi-Winner Approval Voting

In many real world situations, collective decisions are made using voting. Moreover, scenarios such as committee or board elections require voting rules that return multiple winners. In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish. Winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most votes. An agent may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics to strategize, instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in multi-winner approval voting scenarios with complete information. We show that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. Instead, voters tend to prioritize the candidates with the highest utilities. Using simulations, we demonstrate the effectiveness of these heuristics in situations where agents only have access to partial information

How Well Do Doodle Polls Do?

Web-based Doodle polls, where respondents indicate their availability for a collection of times provided by the poll initiator, are an increasingly common way of selecting a time for an event or meeting. Yet group dynamics can markedly influence an individual’s response, and thus the overall solution quality. Via theoretical worst-case analysis, we analyze certain common behaviors of Doodle poll respondents, including when participants are either more generous with or more protective of their time, showing that deviating from one’s “true availability” can have a substantial impact on the overall quality of the selected time. We show perhaps counter-intuitively that being more generous with your time can lead to inferior time slots being selected, and being more protective of your time can lead to superior time slots being selected. We also bound the improvement and degradation of outcome quality under both types of behaviors

Modeling Voters in Multi-Winner Approval Voting

In many real world situations, collective decisions are made using voting and, in scenarios such as committee or board elections, employing voting rules that return multiple winners. In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most approvals. In many scenarios, an agent may manipulate the ballot they submit in order to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. There are a number of predictive models of agent behavior in the COMSOC and psychology literature that are based on cognitively plausible heuristic strategies. We show that the existing approaches do not adequately model real-world data. We propose a novel model that takes into account the size of the winning set and human cognitive constraints, and demonstrate that this model is more effective at capturing real-world behaviors in multi-winner approval voting scenarios.Comment: 9 pages, 4 figures. To be published in the Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 202

Stategic Candidacy with Keen Candidates

Presented at the Games, Agents and Incentives WorkshopIn strategic candidacy games, both voters and candidates have preferences over the set of candidates, and candidates make strategic decisions about whether to run an electoral campaign or withdraw from the election, in order to manipulate the outcome according to their preferences. In this work, we extend the standard model of strategic candidacy games to scenarios where candidates may find it harmful for their reputation to withdraw from the election and would only do so if their withdrawal changes the election outcome for the better; otherwise, they would be keen to run the campaign. We study the existence and the quality of Nash equilibria in the resulting class of games, both analytically and empirically, and compare them with the Nash equilibria of the standard model. Our results demonstrate that while in the worst case there may be none or multiple, bad quality equilibria, on average, these games have a unique, optimal equilibrium state

Heuristic Strategies in Uncertain Approval Voting Environments

In many collective decision making situations, agents vote to choose an alternative that best represents the preferences of the group. Agents may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility. In such situations, it is often assumed that voters will vote truthfully rather than expending the effort to strategize. However, being truthful is just one possible heuristic that may be used. In this paper, we examine the effectiveness of heuristics in single winner and multi-winner approval voting scenarios with missing votes. In particular, we look at heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral experiment, we show that people vote truthfully in some situations and prioritize high utility candidates in others. We examine when these behaviors maximize expected utility and show how the structure of the voting environment affects both how well each heuristic performs and how humans employ these heuristics.Comment: arXiv admin note: text overlap with arXiv:1905.1210

The Influence of Early Respondents: Information Cascade Effects in Online Event Scheduling

Sequential group decision-making processes, such as online event scheduling, can be subject to social influence if the decisions involve individuals’ subjective preferences and values. Indeed, prior work has shown that scheduling polls that allow respondents to see others’ answers are more likely to succeed than polls that hide other responses, suggesting the impact of social influence and coordination. In this paper, we investigate whether this difference is due to information cascade effects in which later respondents adopt the decisions of earlier respondents. Analyzing more than 1.3 million Doodle polls, we found evidence that cascading effects take place during event scheduling, and in particular, that early respondents have a larger influence on the outcome of a poll than people who come late. Drawing on simulations of an event scheduling model, we compare possible interventions to mitigate this bias and show that we can optimize the success of polls by hiding the responses of a small percentage of low availability respondents.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134703/1/Romero et al 2017 (WSDM).pd

Reaching Consensus Under a Deadline

Committee decisions are complicated by a deadline, e.g., the next start of a budget, or the beginning of a semester. In committee hiring decisions, it may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefer to agree on a reasonable, if not necessarily the best, candidate, to avoid unfilled positions. In this paper, we propose a model for the above scenario - Consensus Under a Deadline (CUD)- based on a time-bounded iterative voting process. We provide convergence guarantees and an analysis of the quality of the final decision. An extensive experimental study demonstrates more subtle features of CUDs, e.g., the difference between two simple types of committee member behavior, lazy vs.~proactive voters. Finally, a user study examines the differences between the behavior of rational voting bots and real voters, concluding that it may often be best to have bots play on the voters' behalf