The role of signaling constraints in defining optimal marginal costs of reliable signals

Abstract

The handicap principle was originally proposed to resolve the question of why, in their competition for mates, certain species invest in exaggerated ornaments that are often detrimental to their survival. Zahavi suggested that the traits that are most suitable to serve as signals are precisely those that require the burden of extra investment to increase in magnitude: that burden enables the signal to be correlated with the signaler’s quality. According to his model, the additional investment in signaling results in a functional advantage. It does so by providing more accurate information regarding the signaler as it increases the distinction between males of similar quality. There are a number of formalizations of this model, and experimental studies of the handicap principle have focused on testing them. Nonetheless, there is little consensus whether 1) ensuring reliability requires an additional investment or 2) traits that require a relatively higher investment to increase (have higher marginal costs) are selected as signals over those with lower marginal costs. Here, we present an agent-based mate choice model that quantifies the relative stability of signals with different marginal costs. Our model demonstrates how quality-independent constraints (in signal production and perception) affect the range of marginal costs for which a signal is informative. In turn, receiver preference for informative signals drives the selection of signals according to marginal cost. The presence or absence of signaling constraints can determine the outcome of costly signaling models and, thus, explain the different conclusions of Zahavi’s verbal model and its subsequent formalizations.

Dead rats, dopamine, performance metrics, and peacock tails: Proxy failure is an inherent risk in goal-oriented systems

“…This appears to have driven school grade inflation (McCoy & Haig, 2020), and it is in fact the natural consequence in any system in which proxy performance matters on a relative rather than an absolute scale (Frank, 2011). Some proxies may remain informative, even as they are undergoing inflation, such that the pressure to diverge never leads to actual proxy failure (Frank, 2011; Harris, Daon, & Nanjundiah, 2020). In other cases, divergence may be precluded because proxies are directly causally linked to the goal, as is assumed for honest (unfakeable) “index” signals in biology.…”

“…So what constrains proxy failure in ecology – which forces can keep signals honest (Andersson & Simmons, 2006; Connelly, Certo, Ireland, & Reutzel, 2011; Harris et al, 2020; Weaver et al, 2018)? We have already noted the controversial nature of questions regarding when or even if ecological signals are honest (Box 5).…”

“…Other proxies may inflate and then remain stable, even though they have become less informative, such as inflated grades (McCoy & Haig, 2020), publication metrics (Biagioli & Lippman, 2020), or “aesthetic signals” (Prum, 2012). Still other proxies may be impossible to inflate because they are causally linked to the goal (index signals) or may remain informative even while inflating (costly signals; Harris et al, 2020; Zahavi, Butlin, Guilford, & Krebs, 1993). Crucially, any change in a proxy or incorporation of a new proxy by the regulator gives rise to novel affordances for the agent, which can lead to new rounds of hacking and counter measures.…”

The determinants of proxy treadmilling in evolutionary models of reliable signals

“…As model parameters, we include components that are commonly included in biological signalling models, and were already introduced in Fisher's runaway selection model (1930): (i) The degree to which the observer has a preference for the signal (e.g., the peahen's preference of long tails), p ; (ii) the investment in the signal (e.g., elaboration of the peacock's tail), s ; and (iii) the marginal cost of the signal (e.g., the survival cost associated with longer tails), m . As opposed to signal preference and investment, marginal cost often only appears implicitly (as in Fisher's model) or as a fixed property of the signal (Biernaskie, Perry, & Grafen, 2018; Harris, Daon, & Nanjundiah, 2020). By contrast, Goodhart's law subsumes the marginal cost into what determines the signal level; “proxy optimisation” in the target article refers both to an increase in signal level and a reduction in production cost.…”

“…The interaction between the components is based on a previous model that tracks the inflation of signals (Harris et al, 2020). We assume that preference of a signal (e.g., mate selection based on signal level) can increase signal investment, s , at rate Δ s , and that this investment is constrained by the marginal cost, m .…”