Great Hall Meeting Room II

at Ohio Union

Jul 27 @ 09:00 UTC - Jul 27 @ 10:20 UTC

Public session

Jul 27 @ 10:40 UTC - Jul 27 @ 12:20 UTC

Public session

Understanding and modeling information and influence operation effects Information and influence operations are not new, but are a growing concern due to advancements in technology enabling the sharing of information with little cost. Experimental research in this area has started to build a foundation to understand how such operations exploit human’s cognitive vulnerabilities. However, there are mixed findings, a lack of consistency, and a stronger focus on artificial tasks that may not generalize to the real world. Furthermore, few computational models exist to predict, explain, and simulate information effects in individuals and models at the group level focus on information as a contagion. In order to make significant progress in understanding information and influence effects, we need a comprehensive and general theory capable of spanning from individuals to social networks. We need strong and consistent experimental methods, wider data collection, computational models, and realistic experimental materials. This symposium aims to bring together the ICCM community to have meaningful discussions about novel ideas for experiment designs and development of computational models capable of simulating information and influence effects within individuals and groups. Talks will include relevant research, novel ideas and frameworks to address research gaps, and discussion about how we can overcome challenges through collaborations.

Jul 27 @ 15:00 UTC - Jul 27 @ 16:00 UTC

Public session

Psychological researchers apply quantitative models to discover the structure of their construct of interest, typically relying on the fit of the model to the data to reach their conclusions. It has been argued, however, that fit of the model is not sufficient to shield one from model misspecification, that is from inaccurately representing the underlying psychological process of interest. Yet it is unclear to which extent misspecified models can be estimated reliably, and whether the reliability with which parameters can be estimated may signal such misspecification. In this work, we simulate and estimate a whole range of correctly specified and misspecified models, going from the inclusion/exclusion of interaction effects to the aggregation across heterogeneous populations to a linear/nonlinear structure of the model. For each of these models, we then compute the reliability of the parameter estimation for the two types of models and compared the results. We predict that misspecified models may still yield parameter estimates with acceptable reliability metrics when evaluated in isolation. Additionally, we predict that when compared directly to correctly specified models, these same misspecified models will exhibit distinctive reliability degradation patterns reflecting compensatory mechanisms. This dual perspective highlights that while misspecified models might appear adequate when judged solely on standard reliability metrics, comparative analysis against well-specified alternatives can reveal systematic differences that signal model-process misalignment.

Jul 28 @ 09:00 UTC - Jul 28 @ 10:20 UTC

Public session

Jul 28 @ 10:40 UTC - Jul 28 @ 12:00 UTC

Public session

Jul 28 @ 15:00 UTC - Jul 28 @ 16:20 UTC

Public session