Comments on “<scp>AI</scp> and the advent of the cyborg behavioral scientist”

Journal of Consumer Psychology · 2025-03-16 · 7 citations

Abstract Below are comments on Tomaino, Cooke, and Hoover by four teams of collaborative reviewers that helped clarify and focus its original version. Their comments on the refined version articulate how the fast‐moving world of generative AI can alter authors, readers, reviewers, and consumer behavior journals. In the first comment, Blythe, Kulis, and McGraw propose that Generative AI requires substantial effort to generate research that is fast, cost‐effective, and of high quality. They articulate three recommendations: to ask, to train, and to check the system. Asking builds on GenAI's ability to reveal its own capabilities at different stages of the research process. Training allows the system to be customized with relevant context, domain‐specific documents, and tailored examples, enhancing its accuracy and reducing errors. Checking is strongly advised to validate that the outputs are both reasonable and robust. Haenlein, Hewett, and Yoo build on the capabilities of Large Language Models that go beyond the research practices central to consumer psychology. They outline strategic prompting strategies: starting broadly and gradually narrowing to specific domains, downloading information from relevant articles and data that is unlikely to be part of the current corpus, and evoking specific theories, methods, or presentation formats. They also elaborate on the ways the apparent magic of GenAI may raise learning or ethical challenges. The third comment by Stacy Wood focuses less on the capabilities of GenAI and more on how its adoption will depend on researcher feelings—in other words, how different aspects of its use may alter researchers' experiences of doing research and their identities as scholars. GenAI has the potential to both build (through increased productivity or increased accessibility) and limit (through loss of agency or faster production) pride of purpose in research. She argues that feelings from using GenAI are likely to differ across research steps, from developing novel concepts, processes, analyses, and writing of the paper. Wherever GenAI may lessen the excitement, satisfaction, motivation, and perceived status of the researcher, barriers to its use are likely to be erected. Finally, Vicki Morwitz identifies new AI capabilities beyond those explored in Tomaino et al. Those include the ability to generate synthetic data that can guide empirical experiments, a facility to create audio and visual stimuli, a capability to study group behavior, and a capacity to reliably interpret complex human statements. The comment then closes with important questions for editorial policies, raising issues about limitations on AI use by authors, its appropriate applications by review teams, and possible publishers' restrictions on uploading copyrighted articles.

Digital Twins as Funhouse Mirrors: Five Key Distortions

arXiv (Cornell University) · 2025-09-23

Scientists and practitioners are increasingly moving to deploy digital twins--LLM-based models of real individuals--across social science and policy research. We conduct 19 pre-registered studies spanning 164 diverse outcomes (e.g., attitudes toward hiring algorithms, intentions to share misinformation), comparing human responses to those of their corresponding digital twins, which are trained on each individual's prior responses to over 500 questions. We establish an empirical benchmark for digital twin performance: their predictions are only modestly more accurate than those of a homogeneous base LLM and exhibit weak correlation with human responses (average $r = 0.20$). To inform future development, we identify five systematic distortions in digital twin behavior: (i) insufficient individuation, (ii) stereotyping, (iii) representation bias, (iv) ideological bias, and (v) hyper-rationality. Finally, we release our full dataset and code as a standardized testbed for evaluating and improving digital twin methodologies. Together, our findings caution against premature deployment while laying the groundwork for a transparent, replicable, and iterative science of responsible digital twin development.

Numerical Cognition and Behavioral Pricing: The AC-ME Framework

SSRN Electronic Journal · 2025-01-01

Algorithmic pricing: Implications for marketing strategy and regulation

International Journal of Research in Marketing · 2025-05-01 · 11 citations

Over the past decade, a growing number of firms have delegated pricing decisions to algorithms in consumer and business markets such as travel, entertainment, and retail, as well as in platform markets such as ride-sharing. We define algorithmic pricing as “the use of programs to automate the setting of prices.” Firms adopt algorithmic pricing to optimize their prices in response to changing market conditions and to leverage the efficiency gains from automation. Advances in information technology and the increased availability of digital data have further facilitated the use of algorithm-driven pricing strategies. Yet adopting algorithmic pricing is not merely a technical upgrade — it is a strategic decision that must align with a company’s existing and future marketing strategies. Moreover, algorithmic pricing can raise various regulatory concerns regarding potential threats to competition and the legality of price discrimination. This paper discusses the implementation of algorithmic pricing in the context of firms’ marketing strategies and regulatory frameworks, while outlining an agenda for future research in this increasingly important area

Bounded rationalization: The role of acceptance in postchoice and postassignment rationalization.

Psychological Review · 2025-12-04

= 2,557) challenge this view, bridging dissonance and other theories of rationalization. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Algorithmic Pricing: Implications for Consumers, Managers, and Regulators

SSRN Electronic Journal · 2024-01-01 · 2 citations

Algorithmic Pricing: Implications for Marketing Strategy and Regulation

National Bureau of Economic Research · 2024-06-01 · 2 citations

Over the past decade, an increasing number of firms have delegated pricing decisions to algorithms in consumer markets such as travel, entertainment, and retail; business markets such as digital advertising; and platform markets such as ride-sharing.This trend, driven primarily by the increased availability of digital data and developments in information technology, has economic and social consequences that are not yet well understood.The aim of this paper is therefore to examine various implications and challenges of algorithmic pricing for consumers, managers, and regulators.We contribute to the literature by defining and classifying algorithmic pricing, understanding managers' perceptions and adding empirical evidence on its use, raising important considerations for the three stakeholders, and finally outlining research priorities in this area.

Widespread misestimates of greenhouse gas emissions suggest low carbon competence

Nature Climate Change · 2024-06-21 · 44 citations

Digital Therapy for Negative Consumption Experiences: The Impact of Emotional and Rational Reviews on Review Writers

Journal of Consumer Research · 2024-06-14 · 6 citations

Abstract This research tests a solution for consumers to recover faster from negative experiences. We identify this solution by examining how the manner in which review writers express their emotions and rational thoughts in their reviews causally influences review writers. The results of five studies (field data and experiments) show that, similar to writing about traumatic life events, when review writers express both emotional and rational aspects in reviews about negative consumption experiences, they feel better afterwards (i.e., they recover affectively), and are more likely to purchase again (i.e., they recover cognitively). We further examine why writing integrated reviews has positive effects on review writers by collecting biophysiological response data, which provide support for an account related to affective recovery, and by analyzing thought listing data, which provide support for an account related to cognitive recovery. This research shows that writing online reviews can serve as a digital therapy tool that helps consumers recover from negative experiences.

Algorithmic Pricing: Implications for Consumers, Managers, and Regulators

SSRN Electronic Journal · 2024-01-01 · 5 citations