Advancing Methodology for Social Science Research Using Alternate
Reality Games: Proof-of-Concept Through Measuring Individual Differences and
Adaptability and their impact on Team Performance
- URL: http://arxiv.org/abs/2106.13740v1
- Date: Fri, 25 Jun 2021 16:22:22 GMT
- Title: Advancing Methodology for Social Science Research Using Alternate
Reality Games: Proof-of-Concept Through Measuring Individual Differences and
Adaptability and their impact on Team Performance
- Authors: Magy Seif El-Nasr, Casper Harteveld, Paul Fombelle, Truong-Huy Nguyen,
Paola Rizzo, Dylan Schouten, Abdelrahman Madkour, Chaima Jemmali, Erica
Kleinman, Nithesh Javvaji, Zhaoqing Teng, Extra Ludic Inc
- Abstract summary: We discuss work tackling an open problem with a focus on understanding individual differences and its effect on team adaptation.
We specifically discuss our contribution in terms of methods that augment survey data and behavioral data.
To make this problem more tractable we chose to focus on specific types of environments, Alternate Reality Games (ARGs)
- Score: 8.680283375719466
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While work in fields of CSCW (Computer Supported Collaborative Work),
Psychology and Social Sciences have progressed our understanding of team
processes and their effect performance and effectiveness, current methods rely
on observations or self-report, with little work directed towards studying team
processes with quantifiable measures based on behavioral data. In this report
we discuss work tackling this open problem with a focus on understanding
individual differences and its effect on team adaptation, and further explore
the effect of these factors on team performance as both an outcome and a
process. We specifically discuss our contribution in terms of methods that
augment survey data and behavioral data that allow us to gain more insight on
team performance as well as develop a method to evaluate adaptation and
performance across and within a group. To make this problem more tractable we
chose to focus on specific types of environments, Alternate Reality Games
(ARGs), and for several reasons. First, these types of games involve setups
that are similar to a real-world setup, e.g., communication through slack or
email. Second, they are more controllable than real environments allowing us to
embed stimuli if needed. Lastly, they allow us to collect data needed to
understand decisions and communications made through the entire duration of the
experience, which makes team processes more transparent than otherwise
possible. In this report we discuss the work we did so far and demonstrate the
efficacy of the approach.
Related papers
- Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students [3.0294711465150006]
The study highlights the collaboration trends that emerge as first-semester students develop a 2D game project.
Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes.
Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves.
arXiv Detail & Related papers (2024-10-28T11:42:05Z) - External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling [3.536024441537599]
Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments.
We propose an agent influence framework for RL agents to improve the adaptation efficiency of external models in changing environments.
Our results show that our method outperforms the baselines in terms of external model adaptation on metrics that measure both efficiency and performance.
arXiv Detail & Related papers (2024-06-28T23:31:22Z) - Context Retrieval via Normalized Contextual Latent Interaction for
Conversational Agent [3.9635467316436133]
We present a novel method, PK-NCLI, that is able to accurately and efficiently identify relevant auxiliary information to improve the quality of conversational responses.
Our experimental results indicate that PK-NCLI outperforms the state-of-the-art method, PK-FoCus, in terms of perplexity, knowledge grounding, and training efficiency.
arXiv Detail & Related papers (2023-12-01T18:53:51Z) - Detecting and Optimising Team Interactions in Software Development [58.720142291102135]
This paper presents a data-driven approach to detect the functional interaction structure for software development teams.
Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model.
We show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios.
arXiv Detail & Related papers (2023-02-28T14:53:29Z) - A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based
Policy Learning [11.998708550268978]
We develop a class of solutions for open ad hoc teamwork under full and partial observability.
We show that our solution can learn efficient policies in open ad hoc teamwork in fully and partially observable cases.
arXiv Detail & Related papers (2022-10-11T13:44:44Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z) - Perceiving the World: Question-guided Reinforcement Learning for
Text-based Games [64.11746320061965]
This paper introduces world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment.
We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency.
arXiv Detail & Related papers (2022-03-20T04:23:57Z) - Inverse Online Learning: Understanding Non-Stationary and Reactionary
Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions.
By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem.
We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them.
Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z) - Scaling up Search Engine Audits: Practical Insights for Algorithm
Auditing [68.8204255655161]
We set up experiments for eight search engines with hundreds of virtual agents placed in different regions.
We demonstrate the successful performance of our research infrastructure across multiple data collections.
We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time.
arXiv Detail & Related papers (2021-06-10T15:49:58Z) - On Emergent Communication in Competitive Multi-Agent Teams [116.95067289206919]
We investigate whether competition for performance from an external, similar agent team could act as a social influence.
Our results show that an external competitive influence leads to improved accuracy and generalization, as well as faster emergence of communicative languages.
arXiv Detail & Related papers (2020-03-04T01:14:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.