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Interactions between multiple ecosystem stressors are expected to jeopardize biological processes, functions and biodiversity. The scientific community has declared stressor interactions—notably synergies—a key issue for conservation and management. Here, we review ecological literature over the past four decades to evaluate trends in the reporting of ecological interactions (synergies, antagonisms and additive effects) and highlight the implications and importance to conservation. Despite increasing popularity, and ever-finer terminologies, we find that synergies are (still) not the most prevalent type of interaction, and that conservation practitioners need to appreciate and manage for all interaction outcomes, including antagonistic and additive effects. However, it will not be possible to identify the effect of every interaction on every organism's physiology and every ecosystem function because the number of stressors, and their potential interactions, are growing rapidly. Predicting the type of interactions may be possible in the near-future, using meta-analyses, conservation-oriented experiments and adaptive monitoring. Pending a general framework for predicting interactions, conservation management should enact interventions that are robust to uncertainty in interaction type and that continue to bolster biological resilience in a stressful world.

We examine how CEOs can facilitate the development of investor trust that helps mitigate the effects of negative information. Results from an experiment show that investors trust the CEO more and are more willing to invest in the firm when the CEO communicates firm news followed by a negative earnings surprise through a personal Twitter account than when the news and surprise comes from the CEO via a website or from the firm's Investor Relations Twitter account or website. A follow-up experiment shows that repeating the negative news does not incrementally affect investors who received the news from the CEO's Twitter account, but does further negatively impact investors who received the news via other disclosure mediums, especially those who received the news via the Investor Relations Twitter account. Our results have implications for firms and executives considering the costs and benefits of communicating with investors via Twitter.

Distinguishing between meaningful and meaningless sensory information is fundamental to forming accurate representations of the world. Dopamine is thought to play a central role in processing the meaningful information content of observations, which motivates an agent to update their beliefs about the environment. However, direct evidence for dopamine’s role in human belief updating is lacking. We addressed this question in healthy volunteers who performed a model-based fMRI task designed to separate the neural processing of meaningful and meaningless sensory information. We modeled participant behavior using a normative Bayesian observer model and used the magnitude of the model-derived belief update following an observation to quantify its meaningful information content. We also acquired PET imaging measures of dopamine function in the same subjects. We show that the magnitude of belief updates about task structure (meaningful information), but not pure sensory surprise (meaningless information), are encoded in midbrain and ventral striatum activity. Using PET we show that the neural encoding of meaningful information is negatively related to dopamine-2/3 receptor availability in the midbrain and dexamphetamine-induced dopamine release capacity in the striatum. Trial-by-trial analysis of task performance indicated that subclinical paranoid ideation is negatively related to behavioral sensitivity to observations carrying meaningful information about the task structure. The findings provide direct evidence implicating dopamine in model-based belief updating in humans and have implications for understating the pathophysiology of psychotic disorders where dopamine function is disrupted.

Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement-based and 2 cash flow statement-based analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns. No balance sheet or expense surprises are significant. The most important surprises are (i) one-quarter-ahead sales guidance surprise, (ii) analyst sales surprise, (iii) annual Street earnings guidance surprise, and (iv) analyst Street earnings surprise. We also find that the adjusted R2s of our multivariate regressions are three times higher than the adjusted R2s of univariate Street earnings surprise regressions, and that the four most important surprises account for approximately half of this increase in explanatory power.

Central bank announcements simultaneously convey information about monetary policy and the central bank's assessment of the economic outlook. This paper disentangles these two components and studies their effect on the economy using a structural vector autoregression. It relies on the information inherent in high-frequency co-movement of interest rates and stock prices around policy announcements: a surprise policy tightening raises interest rates and reduces stock prices, while the complementary positive central bank information shock raises both. These two shocks have intuitive and very different effects on the economy. Ignoring the central bank information shocks biases the inference on monetary policy nonneutrality.

Black swans are improbable events that nonetheless occur—often with profound consequences. Such events drive important transitions in social systems (e.g., banking collapses) and physical systems (e.g., earthquakes), and yet it remains unclear the extent to which ecological population numbers buffer or suffer from such extremes. Here, we estimate the prevalence and direction of black-swan events (heavy-tailed process noise) in 609 animal populations after accounting for population dynamics (productivity, density dependence, and typical stochasticity). We find strong evidence for black-swan events in ∼4% of populations. These events occur most frequently for birds (7%), mammals (5%), and insects (3%) and are not explained by any life-history covariates but tend to be driven by external perturbations such as climate, severe winters, predators, parasites, or the combined effect of multiple factors. Black-swan events manifest primarily as population die-offs and crashes (86%) rather than unexpected increases, and ignoring heavy-tailed process noise leads to an underestimate in the magnitude of population crashes. We suggest modelers consider heavy-tailed, downward-skewed probability distributions, such as the skewed Student t used here, when making forecasts of population abundance. Our results demonstrate the importance of both modeling heavy-tailed downward events in populations, and developing conservation strategies that are robust to ecological surprises.

Most statistical models assume linearity and few variable interactions, even though real‐world ecological patterns often result from nonlinear and highly interactive processes. We here introduce a set of novel empirical modeling techniques which can address this mismatch: statistically reinforced machine learning. We demonstrate the behaviors of three techniques (conditional inference tree, model‐based tree, and permutation‐based random forest) by analyzing an artificially generated example dataset that contains patterns based on nonlinearity and variable interactions. The results show the potential of statistically reinforced machine learning algorithms to detect nonlinear relationships and higher‐order interactions. Estimation reliability for any technique, however, depended on sample size. The applications of statistically reinforced machine learning approaches would be particularly beneficial for investigating (1) novel patterns for which shapes cannot be assumed a priori, (2) higher‐order interactions which are often overlooked in parametric statistics, (3) context dependency where patterns change depending on other conditions, (4) significance and effect sizes of variables while taking nonlinearity and variable interactions into account, and (5) a hypothesis using parametric statistics after identifying patterns using statistically reinforced machine learning techniques.

How do individuals form expectations about future inflation? We propose that individuals overweight inflation experienced during their lifetimes. This approach modifies existing adaptive learning models to allow for age-dependent updating of expectations in response to inflation surprises. Young individuals update their expectations more strongly than older individuals since recent experiences account for a greater share of their accumulated lifetime history. We find support for these predictions using 57 years of microdata on inflation expectations from the Reuters/Michigan Survey of Consumers. Differences in experiences strongly predict differences in expectations, including the substantial disagreement between young and old individuals in periods of highly volatile inflation, such as the 1970s. It also explains household borrowing and lending behavior, including the choice of mortgages.

Using data from Taiwan, where listed firms are required to disclose monthly revenues, this paper examines the information value of mandatory interim revenue disclosures. We find that monthly revenue surprises have a significant effect on analysts’ earnings forecasts, suggesting that analysts incorporate such information into their earnings forecasts once the information is available. In addition, monthly revenue surprises significantly predict future earnings surprises, and their predictive power goes beyond the information provided by analysts’ earnings forecasts, suggesting that monthly revenue surprises provide leading information about future earnings growth but analysts do not fully reflect this information. Stock prices drift positively with monthly revenue surprises during the period prior to the quarterly earnings announcement. However, when quarterly earnings are finally announced, stock prices are no longer driven by monthly revenue surprises, suggesting that monthly revenue surprises have been fully incorporated into the stock prices. Overall, our results suggest that interim accounting information helps investors increase the speed of adjustments to fundamental news.

Today, in the era of the spoiler alert, \"surprise\" in fiction is primarily associated with an unexpected plot twist, but in earlier usage, the word had darker and more complex meanings. Originally denoting a military ambush or physical assault, surprise went through a major semantic shift in the eighteenth century: from violent attack to pleasurable experience, and from external event to internal feeling. InSurprise, Christopher R. Miller studies that change as it took shape in literature ranging fromParadise Lostthrough the novels of Jane Austen. Miller argues that writers of the period exploited and arbitrated the dual nature of surprise in its sinister and benign forms. Even as surprise came to be associated with pleasure, it continued to be perceived as a problem: a sign of ignorance or naïveté, an uncontrollable reflex, a paralysis of rationality, and an experience of mere novelty or diversion for its own sake. In close readings of exemplary scenes-particularly those involving astonished or petrified characters-Miller shows how novelists sought to harness the energies of surprise toward edifying or comic ends, while registering its underpinnings in violence and mortal danger. In the Roman poet Horace's famous axiom, poetry should instruct and delight, but in the early eighteenth century, Joseph Addison signally amended that formula to suggest that the imaginative arts should surprise and delight. Investigating the significance of that substitution, Miller traces an intellectual history of surprise, involving Aristotelian poetics, Cartesian philosophy, Enlightenment concepts of the passions, eighteenth-century literary criticism and aesthetics, and modern emotion theory. Miller goes on to offer a fresh reading of what it means to be \"surprised by sin\" inParadise Lost, showing how Milton's epic both harks back to the symbolic functions of violence in allegory and looks ahead to the moral contours of the novel. Subsequent chapters study the Miltonic ramifications of surprise in the novels of Defoe, Haywood, Richardson, Fielding, and Sterne, as well as in the poems of Wordsworth and Keats. By focusing on surprise in its inflections as emotion, cognition, and event, Miller's book illuminates connections between allegory and formal realism, between aesthetic discourse and prose fiction, and between novel and lyric; and it offers new ways of thinking about the aesthetic and ethical dimensions of the novel as the genre emerged in the eighteenth century.