9 research outputs found

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

    Get PDF
    This paper considers trial-offer markets where consumer preferences are modelled by a multinomial logit with social influence and position bias. The social signal for a product is given by its current market share raised to power r (or, equivalently, the number of purchases raised to the power of r). The paper shows that, when r is strictly between 0 and 1, and a static position assignment (e.g., a quality ranking) is used, the market converges to a unique equilibrium where the market shares depend only on product quality, not their initial appeals or the early dynamics. When r is greater than 1, the market becomes unpredictable. In many cases, the market goes to a monopoly for some product: which product becomes a monopoly depends on the initial conditions of the market. These theoretical results are complemented by an agent-based simulation which indicates that convergence is fast when r is between 0 and 1, and that the quality ranking dominates the well-known popularity ranking in terms of market efficiency. These results shed a new light on the role of social influence which is often blamed for unpredictability, inequalities, and inefficiencies in markets. In contrast, this paper shows that, with a proper social signal and position assignment for the products, the market becomes predictable, and inequalities and inefficiencies can be controlled appropriately

    Aligning Popularity and Quality in Online Cultural Markets

    No full text
    Social influence is ubiquitous in cultural markets and plays an important role in recommendations for books, songs, and news articles to name only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability. Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which recovers product quality and ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy quickly identifies "blockbusters", outperforms other policies, and is highly predictable

    Monte Carlo simulations and ODE solutions of the market shares for symmetric appeals.

    No full text
    <p>The Monte Carlo simulations involved 10<sup>4</sup> realizations of the system for nine different set of parameters (grey lines). In all cases we used <i>q</i><sub>1</sub> = 1, the appeal used for both products were the same (<i>A</i><sub>1</sub> = <i>A</i><sub>2</sub>) and both products start with zero purchases, <i>d</i><sub>1</sub>(<i>t</i> = 0) = <i>d</i><sub>2</sub>(<i>t</i> = 0) = 0. Although the Monte Carlo simulations produce discrete dots in the (<i>d<sub>T</sub></i>, <i>MS</i><sub>2</sub>) space, we plot each simulation with straight lines that link consecutive dots to follow trajectories easily.</p

    Monte Carlo simulations and ODE solutions of the market shares for symmetric appeals.

    No full text
    <p>The Monte Carlo simulations involved 10<sup>4</sup> realizations of the system for nine different set of parameters (grey lines). In all cases we used <i>q</i><sub>1</sub> = 1, the appeal used for both products were the same (<i>A</i><sub>1</sub> = <i>A</i><sub>2</sub>) and both products start with zero purchases, <i>d</i><sub>1</sub>(<i>t</i> = 0) = <i>d</i><sub>2</sub>(<i>t</i> = 0) = 0. Although the Monte Carlo simulations produce discrete dots in the (<i>d<sub>T</sub></i>, <i>MS</i><sub>2</sub>) space, we plot each simulation with straight lines that link consecutive dots to follow trajectories easily.</p

    Market share of product 2 (<i>MS</i><sub>2</sub>) as a function of <i>Q</i><sub>2</sub> and <i>A</i><sub>2</sub>, for different values of <i>A</i><sub>1</sub> and , assuming <i>q</i><sub>1</sub> = 1.

    No full text
    <p>Market share of product 2 (<i>MS</i><sub>2</sub>) as a function of <i>Q</i><sub>2</sub> and <i>A</i><sub>2</sub>, for different values of <i>A</i><sub>1</sub> and , assuming <i>q</i><sub>1</sub> = 1.</p

    Monte carlo simulations and ODE solutions of the market shares for asymetric appeals.

    No full text
    <p>The Monte Carlo simulations involved 10<sup>4</sup> realizations of the system for nine different sets of parameters (grey lines). In all cases, <i>q</i><sub>1</sub> = 1 and both products start with zero purchases, i.e., <i>d</i><sub>1</sub>(<i>t</i> = 0) = <i>d</i><sub>2</sub>(<i>t</i> = 0) = 0. Although Monte Carlo simulations produce discrete dots in the (<i>d<sub>T</sub></i>, <i>MS</i><sub>2</sub>) space, we plot each simulation with straight lines that link consecutive dots to follow trajectories easily.</p
    corecore