An attention economy model of co-evolution between content quality and audience selectivity

TL;DR

The paper develops a minimal two-population evolutionary framework to study how content quality and audience attention coevolve under limited attention capacity. It models content providers as high- or low-effort (H/L) and consumers as active or passive (A/P), with attention budget $\sigma$, reward per view $r$, production costs $c_H$, $c_A$, and quality benefits $b_H>b_L$, occurring in a piecewise-smooth replicator system driven by the interaction between supply and selective attention. The analysis identifies three regimes—collapse, boundary, and coexistence—sharply delineated by the discriminability condition $b m \sigma (1-\sigma) > c_A$ and the relative reward-to-cost balance, yielding an interior coexistence equilibrium or a boundary state when non-collapsed dynamics persist; otherwise, the system collapses to low-quality, non-selective content. These findings imply that sustaining high-quality content hinges on maintaining audience discriminability and aligning incentives to promote high-effort creation, with policy implications for labeling, curation, and incentive design to avert informational degradation in the attention economy.

Abstract

Human attention has become a scarce and strategically contested resource in digital environments. Content providers increasingly engage in excessive competition for visibility, often prioritizing attention-grabbing tactics over substantive quality. Despite extensive empirical evidence, however, there is a lack of theoretical models that explain the fundamental dynamics of the attention economy. Here, we develop a minimal mathematical framework to explain how content quality and audience attention coevolve under limited attention capacity. Using an evolutionary game approach, we model strategic feedback between providers, who decide how much effort to invest in production, and consumers, who choose whether to search selectively for high-quality content or to engage passively. Analytical and numerical results reveal three characteristic regimes of content dynamics: collapse, boundary, and coexistence. The transitions between these regimes depend on how effectively audiences can distinguish content quality. When audience discriminability is weak, both selective attention and high-quality production vanish, leading to informational collapse. When discriminability is sufficient and incentives are well aligned, high- and low-quality content dynamically coexist through feedback between audience selectivity and providers' effort. These findings identify two key conditions for sustaining a healthy information ecosystem: adequate discriminability among audiences and sufficient incentives for high-effort creation. The model provides a theoretical foundation for understanding how institutional and platform designs can prevent the degradation of content quality in the attention economy.

Figures (4)

• Figure 1: Conceptual diagram of the attention economy model. Providers invest high (H) or low (L) effort in content creation, while consumers allocate limited attention $(\sigma)$ through active (A) or passive (P) viewing. Their strategic feedback determines the evolution of content quality and attention distribution.
• Figure 2: Representative trajectories of the replicator dynamics under three parameter regimes. Parameters were chosen to satisfy the corresponding stability conditions: (a,b) Collapse: $m=20$, $n=100$, $\sigma=0.5$, $r=1$, $b_H-b_L=1$, $c_A=10$, $c_H=2$. (c,d) Boundary: $m=1$, $n=10$, $\sigma=0.5$, $r=0.12$, $b_H-b_L=10$, $c_A=1$, $c_H=1$. (e,f) Coexistence: $m=20$, $n=100$, $\sigma=0.5$, $r=1$, $b_H-b_L=3$, $c_A=2$, $c_H=2$. The green dashed lines mark the equilibrium.
• Figure 3: Basins of attraction under two parameter regimes. Left: parameter setting where the boundary equilibrium is stable. Right: setting where the coexistence equilibrium is stable. Red regions indicate initial conditions converging to non-collapsed equilibria, whereas gray regions indicate collapse. Background arrows show the replicator vector field. Basins were computed on a 100 $\times$ 100 grid of initial conditions.
• Figure 4: Regimes of collapse and non-collapse. (a) Regimes on the $(b_H - b_L, c_A)$ plane for fixed $\sigma = 0.5$. The dashed line $c_A = m\sigma(b_H - b_L)(1 - \sigma)$ separates collapse (gray, above) from non-collapse (white, below). (b) Regimes on the $(\sigma, c_A)$ plane for fixed $b_H - b_L = 1$. The non-collapsed region is widest around $\sigma \approx 0.5$ and shrinks as $\sigma \to 0$ or $\sigma \to 1$.