# Using computational modeling to formalize an integrated psychosocial theory of loneliness > **NIH NIH F31** · UNIVERSITY OF VIRGINIA · 2024 · $40,237 ## Abstract PROJECT SUMMARY/ABSTRACT High levels of loneliness have become an urgent public health crisis. Though transient loneliness is a normative experience that can promote helpful behaviors and lead to positive outcomes (e.g., social reconnection), when loneliness becomes chronic it is associated with a host of negative health consequences, such as depression and anxiety. Research in cognitive, clinical, and social psychology, sociology, and network science has provided valuable insights into the mechanisms underlying social (dis)connection and loneliness. However, these fields typically conduct research in isolation from one another and have historically used methods that are unlikely to adequately capture the complex biopsychosocial nature of loneliness. Computational modeling offers a promising approach to help address these limitations. Computational modeling can be used to integrate hypotheses generated by different disciplines and rigorously test these theoretical predictions by formalizing them (i.e., representing them in the language of mathematics and/or computer code as opposed to words). For instance, rather than simply stating that chronic loneliness arises from an interaction between social network structure (e.g., number and closeness of friends) and beliefs about social interaction, this prediction can be written as computer code that represents people as interacting agents, driven by specific behavioral rules, that exhibit dynamic changes in cognition as they interact and assess their social relationships in comparison to others’. Such a model can then be used to simulate data that mirrors real- world systems (e.g., a social network comprised of individual people) and the model outcomes can be compared to available empirical findings to determine how well theories of loneliness can explain what is observed in reality, thereby presenting an opportunity to identify and correct erroneous assumptions or gaps in knowledge and theory. This understanding is key to selecting effective intervention targets. The overarching aim of this research is to formalize an integrated psychosocial theory of loneliness. First, a systematic review will be conducted to identify empirically-supported social and cognitive factors related to loneliness. Second, agent-based modeling and differential equations will be used to model individuals’ changing thoughts and behaviors within an evolving social network. Finally, simulated data from the model will be compared to the empirical findings identified in the systematic review. Results will provide a systematic assessment of loneliness theories, help identify novel research questions, and pinpoint targets for intervention and prevention efforts. A training plan consisting of close mentorship, courses and workshops, hands-on learning, and interdisciplinary collaboration has been carefully planned to develop the expertise in mathematical and computational modeling and theories of social relationships necessary to c... ## Key facts - **NIH application ID:** 11068681 - **Project number:** 1F31MH136730-01A1 - **Recipient organization:** UNIVERSITY OF VIRGINIA - **Principal Investigator:** Emma Rose Toner - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $40,237 - **Award type:** 1 - **Project period:** 2025-01-01 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11068681 ## Citation > US National Institutes of Health, RePORTER application 11068681, Using computational modeling to formalize an integrated psychosocial theory of loneliness (1F31MH136730-01A1). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/11068681. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*