Quantifying the dynamics of failure across science, startups and security.

Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation1-7, human dynamics8-11 and learning12-17, we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.

Cell and gene therapy investment: evolution and future outlook on investor perspectives.

BACKGROUND AIMS: To better understand the attitudes and behaviors of investors involved in funding cell and gene therapy (CGT) businesses, the Business Development and Finance) subcommittee of International Society for Cell and Gene Therapy, in collaboration with Truist Securities, conducted a broad survey of the investment community in late 2021. METHODS: This survey follows a similar study that this group executed in 2018, and the longitudinal comparisons between the two time periods provide insights into how investor behavior in the CGT field has evolved. RESULTS: The vast majority of investor respondents are specialist biotech investors who are primarily active in deploying capital in North America and Europe. There was a notable increase in the proportion of investors actively deploying capital in China and Japan between 2018 and 2021. The percentage of respondents' portfolios dedicated to CGT companies has also increased in this period, reflecting a noteworthy trend in the therapeutic landscape. CONCLUSIONS: Clinically significant data remain the dominant force behind investment decisions, whereas competition from other drug modalities has now emerged as the most-cited barrier to making a CGT investment, eclipsing safety concerns as the most significant barrier to investment in 2018. Concerns around manufacturing and scale-up have also increased in prominence amongst the investment community. Gene-editing technologies are attracting investors as the most compelling new CGT technology. This survey also revealed that most investors expect to increase their level of investment in allogeneic technologies relative to autologous products in the coming years.

The joint dynamics of investor beliefs and trading during the COVID-19 crash.

We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: 1) on February 11-12, around the all-time stock market high, 2) on March 11-12, after the stock market had collapsed by over 20%, and 3) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-y) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.

High-resolution land value maps reveal underestimation of conservation costs in the United States.

The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I developed high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimate conservation costs, especially at the expensive tail of the distribution. This has led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges and can help improve the targeting of public ecosystem service investments.

Brain Activity Foreshadows Stock Price Dynamics.

Successful investing is challenging since stock prices are difficult to consistently forecast. Recent neuroimaging evidence suggests, however, that activity in brain regions associated with anticipatory affect may not only predict individual choice, but also forecast aggregate behavior out-of-sample. Thus, in two experiments, we specifically tested whether anticipatory affective brain activity in healthy humans could forecast aggregate changes in stock prices. Using functional magnetic resonance imaging, we found in a first experiment (n = 34, 6 females; 140 trials/subject) that nucleus accumbens activity forecast stock price direction, whereas anterior insula (AIns) activity forecast stock price inflections. In a second preregistered replication experiment (n = 39, 7 females) that included different subjects and stocks, AIns activity still forecast stock price inflections. Importantly, AIns activity forecast stock price movement even when choice behavior and conventional stock indicators did not (e.g., previous stock price movements), and classifier analysis indicated that forecasts based on brain activity should generalize to other markets. By demonstrating that AIns activity might serve as a leading indicator of stock price inflections, these findings imply that neural activity associated with anticipatory affect may extend to forecasting aggregate choice in dynamic and competitive environments such as stock markets.SIGNIFICANCE STATEMENT Many try but fail to consistently forecast changes in stock prices. New evidence, however, suggests that anticipatory affective brain activity may not only predict individual choice, but also may forecast aggregate choice. Assuming that stock prices index collective choice, we tested whether brain activity sampled during the assessment of stock prices could forecast subsequent changes in the prices of those stocks. In two neuroimaging experiments, a combination of previous stock price movements and brain activity in a region implicated in processing uncertainty and arousal forecast next-day stock price changes-even when behavior did not. These findings challenge traditional assumptions of market efficiency by implying that neuroimaging data might reveal "hidden information" capable of foreshadowing stock price dynamics.

Assessing ecological infrastructure investments.

Conventional markets can underprovide ecosystem services. Deliberate creation of a market for ecosystem services [e.g., a payments for ecosystem services (PES) scheme] can close the gap. The new ecosystem service market alters behaviors and quantities of ecosystem service provided and reveals prices for the ecosystems service: a market-clearing equilibrium. Assessing the potential for PES programs, which often act as ecological infrastructure investment mechanisms, requires forecasting the market-clearing equilibrium. Forecasting the equilibrium is complicated, especially at relevant social and ecological scales. It requires greater disciplinary integration than valuing ecosystem services or computing the marginal cost of making a land-use change to produce a service. We conduct an ex ante benefit-cost assessment and forecast market-clearing prices and quantities for ecological infrastructure investment contracts in the Panama Canal Watershed. The Panama Canal Authority could offer contracts to private farmers to change land use to increase dry-season water flow and reduce sedimentation. A feasible voluntary contracting system yields a small program of about 1,840 ha of land conversion in a 279,000-ha watershed and generates a 4.9 benefit-cost ratio. Physical and social constraints limit market supply and scalability. Service delays, caused by lags between the time payments must be made and the time services stemming from ecosystem change are realized, hinder program feasibility. Targeting opportunities raise the benefit-cost ratio but reduce the hectares likely to be converted. We compare and contrast our results with prior state-of-the-art assessments on this system.

Reconciling corruption with conservation triage: Should investments shift from the last best places?

Considerable outside funding will be required to overcome the financial shortfalls faced by most of Africa's protected areas. Given limited levels of external support, it will be essential to allocate these funds wisely. While most recent studies on conservation triage have recommended prioritizing reserves with the highest remaining conservation value (the "last best places"), such investments are complicated by the fact that these same reserves often attract the greatest revenues from ecotourism and thus the most attention from corrupt local governments. Alternatively, philanthropic organizations might achieve greater returns from investing in the management of neglected areas with lower current conservation value but with less financial leakage from corruption. We outline here how high levels of corruption could favor a strategy that shifts investments away from the last best places.

The social value of investing in public health across the life course: a systematic scoping review.

BACKGROUND: Making the case for investing in public health by illustrating the social, economic and environmental value of public health interventions is imperative. Economic methodologies to help capture the social value of public health interventions such as Social Return on Investment (SROI) and Social Cost-Benefit Analysis (SCBA) have been developed over past decades. The life course approach in public health reinforces the importance of investment to ensure a good start in life to safeguarding a safe, healthy and active older age. This novel review maps an overview of the application of SROI and SCBA in the existing literature to identify the social value of public health interventions at individual stages of the life course. METHODS: A systematic scoping review was conducted on peer-reviewed and grey literature to identify SROI and SCBA studies of public health interventions published between January 1996 and June 2019. All primary research articles published in the English language from high-income countries that presented SROI and SCBA outputs were included. Studies were mapped into stages of the life course, and data on the characteristics of the studies were extracted to help understand the application of social value methodology to assess the value of public health interventions. RESULTS: Overall 40 SROI studies were included in the final data extraction, of which 37 were published in the grey literature. No SCBA studies were identified in the search. Evidence was detected at each stage of the life course which included; the birth, neonatal period, postnatal period and infancy (n = 2); childhood and adolescence (n = 17); adulthood (main employment and reproductive years) (n = 8); and older adulthood (n = 6). In addition, 7 studies were identified as cross-cutting across the life course in their aims. CONCLUSION: This review contributes to the growing evidence base that demonstrates the use of social value methodologies within the field of public health. By mapping evidence across stages of the life course, this study can be used as a starting point by public health professionals and institutions to take forward current thinking about moving away from traditional economic measures, to capturing social value when investing in interventions across the life course.