Organizations frequently use behavioral interventions—including incentives, messaging campaigns, and modifications to choice architecture—to influence people’s behavior. However, recent evidence suggests that such interventions often have inconsistent effects across implementations. To understand this variability, we start from the premise that interventions are not simply levers for changing behavior; they also signal information to decision-makers. For example, when an option is set as the default, people may infer that it is recommended by the organization—an inference that can increase the likelihood they stick with that option. But when defaults appear self-serving, people may reject them. Here, we propose a framework to better understand the effects of behavioral interventions by identifying the inferences people draw when encountering them. In developing this framework, we first characterize the defining features of behavioral interventions that determine how they are psychologically experienced. These features implicate five fundamental needs, which guide decision-makers’ information-seeking processes. From these needs we derive a principled typology of inferences drawn by decision-makers in response to behavioral interventions, bringing together scattered evidence for these inferences from across the behavioral sciences. The resulting framework entails predictions about when specific inferences will arise and how these inferences influence the overall effects of behavioral interventions. Utilizing the framework, we provide a tool for enhancing intervention design. Together, the current research offers an integrative framework for advancing theory on behavioral interventions while also outlining actionable insights for organizations hoping to promote behavior change at scale.
Joseph Reiff, Jonathan E. Bogard· Organization science (Provid...· 0 citations
Contemplative traditions have long guided ethical behavior and prosocial interaction, and recent work suggests that contemplative principles (e.g., mindfulness, compassion, non-dual reasoning) may offer a promising paradigm for aligning large language models (LLMs), improving cooperation and reducing ethical violations in LLM outputs. However, as new models, evaluation metrics, and benchmarks emerge rapidly, it remains challenging to systematically assess whether and how contemplative principles enhance LLM alignment across diverse and evolving scenarios, and existing approaches are often ad hoc and fail to generalize. We present a modular, extensible evaluation framework, initially targeted at the mental health domain, that enables seamless integration of new models, metrics, and benchmarks through a reusable pipeline. The framework currently reproduces existing state-of-the-art results and supports systematic cross-evaluation by flexibly mixing and matching models, metrics, and benchmarks, enabling fair comparison and deeper insight. Its plug-and-play prompting module offers a principled pathway for incorporating ethical perspectives such as contemplative principles, allowing domain experts to define alignment criteria without requiring technical expertise. Although initially focused on mental health, the framework is domain-agnostic and extends naturally to areas such as decision-making, moral reasoning, and human-AI collaboration. By bridging computational evaluation with human-centered ethical reasoning, this work lays the groundwork for interdisciplinary research spanning cognitive science, behavioral economics, philosophy, and system design, toward robust, trustworthy, and socially beneficial human-AI ecosystems.
Asher Sprigler, Yang-Yang Feng, Iftach Amir et al.· 0 citations
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