Individuals influence each others' decisions about cultural products such as songs, books, and movies; but to what extent can the perception of success bec...
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
Popularity Feedback Constrains Innovation in Cultural Markets
Real-world creative processes ranging from art to science rely on social feedback-loops between selection and creation. Yet, the effects of popularity feedback on collective creativity remain poorly understood. We investigate how popularity ratings influence cultural dynamics in a large-scale online experiment where participants ($N = 1\,008$) iteratively \textit{select} images from evolving markets and \textit{produce} their own modifications. Results show that exposing the popularity of images reduces cultural diversity and slows innovation, delaying aesthetic improvements. Popularity feedback is associated with changes to both selection and creative stages. During selection, popularity information triggers cumulative advantage, with participants preferentially building upon popular images, reducing diversity. During creation, participants make less disruptive changes, and are more likely to expand existing visual patterns. Feedback loops in cultural markets thus not only shape selection, but also, directly or indirectly, the form and direction of cultural innovation.
Accelerating dynamics of collective attention
With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.
Advertising as a Reminder: Evidence from the Dutch State Lottery
We show that advertising can act as a reminder for consumers who intend to buy a product. , Consumers who intend to buy a product may forget to do so because they suffer from limited attention. Therefore, they may value being reminded by an advertisement. This reminder effect of advertising could be important in many markets but is usually difficult to document. We study it in the context of buying a product that has existed for almost 300 years: a ticket for the Dutch State Lottery. This context is particularly suitable for our analysis because the product is simple, it is very well known, and there are multiple fixed and known purchase cycles per year. Moreover, radio and TV advertisements are designed explicitly to remind consumers to buy a lottery ticket before the draw. This can conveniently be done online. We develop an approach to distinguish reminder effects of advertising from other effects, such as conveying information about the size of the jackpot. The key idea is that reminder effects are short lived. We use minute-level advertising and online sales data and find that the reminder effect of advertising is strong. Reaching 1% of the population by a radio advertisement leads to an increase in online sales of 1.55% in the four hours after the advertisement is aired. For TV advertisements, the increase is 0.78%. We show that the effects generally last longer for radio advertisements. We also provide direct evidence that reminding consumers not only affects the timing of purchases but also leads to market expansion. Finally, we estimate a model of consumer behavior under limited attention to quantify the effect on total sales. We find that total sales would be 16.7% lower without the reminder effect of advertising and that shifting advertising to the week of the draw would lead to a 9.2% increase in sales. History: Puneet Manchanda served as the senior editor and Günter Hitsch served as associate editor for this article. Supplemental Material: A replication package with code and log files and an Online Appendix are available at https://doi.org/10.1287/mksc.2022.1405 .
The Feed Is Fake
That “viral” song, movie, influencer, and celebrity drama you scrolled by recently was likely the result of a stealth marketing campaign.
The Truth About Social Media as an "Advertising Industry," Bluesky's Gamble, and "Reclaiming Conversation" - Nightflight
[Interview] Snow J of Cheerful Music dives into the cross cultural intersection of AI and music – EARMILK
In conversation with Earmilk, Snow. J explores how AI has restructured the foundation of music culture, striking a balance between AI and human-made music as well as Cheerful Music's distinct approach to engaging AI technology in music making and marketing.
Behavioural economics, consumer behaviour and consumer policy: state of the art
Counter to the traditional assumption of neoclassical economics that individuals are rational Homo oeconomici that always seek to maximize their utility and follow their ‘true’ preferences, research in behavioural economics has demonstrated that people's judgements and decisions are often subject to systematic biases and heuristics, and are strongly dependent on the context of the decision. In this article, we briefly review the transition of research from neoclassical economics to behavioural economics, and discuss how the latter has influenced research in consumer behaviour and consumer policy. In particular, we discuss the impacts of key principles such as status quo bias, the endowment effect, mental accounting and the sunk-cost effect, other heuristics and biases related to availability, salience, the anchoring effect and simplicity rules, as well as the effects of other supposedly irrelevant factors such as music, temperature and physical markers on consumers’ decisions. These principles not only add significantly to research on consumer behaviour – they also offer readily available practical implications for consumer policy to nudge behaviour in beneficial directions in consumption domains including financial decision making, product choice, healthy eating and sustainable consumption.
The marketplace of rationalizations
Recent work in economics has rediscovered the importance of belief-based utility for understanding human behaviour. Belief ‘choice’ is subject to an important constraint, however: people can only bring themselves to believe things for which they can find rationalizations. When preferences for similar beliefs are widespread, this constraint generates rationalization markets, social structures in which agents compete to produce rationalizations in exchange for money and social rewards. I explore the nature of such markets, I draw on political media to illustrate their characteristics and behaviour, and I highlight their implications for understanding motivated cognition and misinformation.
The Majority Illusion in Social Networks
Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.
A sampling model of social judgment.
How social media is fueling an intense form of romantic obsession
The digital world is primed to fuel episodes of profound infatuation known as limerence.
Commercial Persuasion in AI-Mediated Conversations
As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.
From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music
Recent text-to-music (TTM) systems such as Suno [22], Udio [23], and Google’s MusicFX [7] allow users to generate music from a natural language prompt, lowering the barrier to music creation for users without specialized musical knowledge [27]. Understanding how users write these prompts is therefore a fundamental question for music information retrieval and human-AI interaction research. Yet, prompting a generative system is not the same cognitive or linguistic act as describing music one has heard [12]. When a user writes a prompt, they encode anticipatory intent to steer the system toward a desired or imagined output, whereas when a listener describes a piece of music, their language is grounded in a perceptual experience. We argue that these two acts produce systematically different language—a distinction that, despite being intuitive, has not been quantitatively examined. This gap has practical consequences: recent TTM models are trained predominantly on metadata-centric corpora (genre labels, BPM, descriptive tags) [6] rather than on the kind of language users naturally produce when listening, leaving the prompt-description gap unexamined. Moreover, this gap limits our ability to evaluate and improve human–AI interaction in music generation, as current systems are optimized for prompt input but commonly assessed through human perception.