The Benefits of Social Influence in Optimized Cultural Markets

Abstract

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies “blockbusters”. Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.

How algorithmic popularity bias hinders or promotes quality

“…In this model, users chose from a list of items ranked by quality rather than popularity; this modification makes the market predictable and aligns popularity and quality. Empirical tests of specific presentation policies combining quality and popularity do suggest that uncertainty can be reduced in this way 28 , 29 . Finally, Becker et al .…”

Popularity signals in trial-offer markets with social influence and position bias

“…it maximises the expected number of downloads. This result was proved by [1] for the performance ranking and by [32] for any static ranking such as the quality ranking. Unfortunately, sublinear social signals are not always beneficial to the market in that sense, as one can see in Example 7.1.…”

“…Abeliuk et al [1] have shown that these values can be recovered accurately and quickly, either before or during the market execution using the approximation suggested by Krumme et al: The objective of the firm running this market is to maximise the total expected number of purchases. To achieve this, the key managerial decision of the firm is what is known as the ranking policy [1], which consists in deciding how to display the products in the market (e.g., where to display a product on a web page). Here we assume that, at the beginning of the market, the firm decides upon a ranking for the items, i.e., an assignment of items to positions in the marketplace.…”

“…In consequence, all the results from this paper can be translated from the φ domain to the µ domain. Table 3 shows the values of the qualities and appeals for the independent setting (obtained from [1]). Table 4 shows the values of the visibilities for each position j ∈ {1, .…”

“…Indeed, various researchers (e.g., [1,3,4,19,32]) indicated that unpredictability and inefficiencies often depend on how products are displayed in the market. In particular, Abeliuk et al [1] show that social influence is always beneficial in expectation when the products are ordered by the performance ranking that maximises the purchases greedily at each step. This result was obtained using the generalised multinomial logit model proposed by Krumme et al [17] to reproduce the MusicLab experiments.…”