Collective gamification uses game mechanics and group dynamics to enhance engagement and raise awareness of important issues, making complex topics more accessible and encouraging real-world action. This approach transforms passive audiences into active participants, amplifying the impact of awareness campaigns. Collective gamification relies on adaptation to keep participants engaged while balancing individual incentives with global goals. Aligning personal motivation with collective objectives is the key to maximizing awareness and driving lasting change. In this paper, starting from a real-world scenario of a sustainable mobility platform we propose a formal model for collective gamification, where users receive task assignments based on their skills and preferences, and are rewarded for completing them. We formulate the problem as a mixed-integer linear program that exposes the meanings and theoretical basis of each constraint and parameter. Then we discuss a hypothetical decentralized implementation for estimating the parameters of the model and solving the optimization problem.

Aligning Individual Motivation with Collective Goals: A Formal Approach to Collective Gamification

Belliato, Riccardo;Bucchiarone, Antonio;Marconi, Annapaola
2025-01-01

Abstract

Collective gamification uses game mechanics and group dynamics to enhance engagement and raise awareness of important issues, making complex topics more accessible and encouraging real-world action. This approach transforms passive audiences into active participants, amplifying the impact of awareness campaigns. Collective gamification relies on adaptation to keep participants engaged while balancing individual incentives with global goals. Aligning personal motivation with collective objectives is the key to maximizing awareness and driving lasting change. In this paper, starting from a real-world scenario of a sustainable mobility platform we propose a formal model for collective gamification, where users receive task assignments based on their skills and preferences, and are rewarded for completing them. We formulate the problem as a mixed-integer linear program that exposes the meanings and theoretical basis of each constraint and parameter. Then we discuss a hypothetical decentralized implementation for estimating the parameters of the model and solving the optimization problem.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/362007
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