The projected impact of the COVID-19 lockdown on breast cancer deaths in England due to the cessation of population screening: a national estimation

BACKGROUND: Population breast screening services in England were suspended in March 2020 due to the COVID-19 pandemic. Here, we estimate the number of breast cancers whose detection may be delayed because of the suspension, and the potential impact on cancer deaths over 10 years. METHODS: We estimated the number and length of screening delays from observed NHS Breast Screening System data. We then estimated additional breast cancer deaths from three routes: asymptomatic tumours progressing to symptomatically diagnosed disease, invasive tumours which remain screen-detected but at a later date, and ductal carcinoma in situ (DCIS) progressing to invasive disease by detection. We took progression rates, prognostic characteristics, and survival rates from published sources. RESULTS: We estimated that 1,489,237 women had screening delayed by around 2–7 months between July 2020 and June 2021, leaving 745,277 outstanding screens. Depending on how quickly this backlog is cleared, around 2500–4100 cancers would shift from screen-detected to symptomatic cancers, resulting in 148–452 additional breast cancer deaths. There would be an additional 164–222 screen-detected tumour deaths, and 71–97 deaths from DCIS that progresses to invasive cancer. CONCLUSIONS: An estimated 148–687 additional breast cancer deaths may occur as a result of the pandemic-related disruptions. The impact depends on how quickly screening services catch up with delays

The wisdom of the crowd playing The Price Is Right

In The Price Is Right game show, players compete to win a prize, by placing bids on its price. We ask whether it is possible to achieve a “wisdom of the crowd” effect, by combining the bids to produce an aggregate price estimate that is superior to the estimates of individual players. Using data from the game show, we show that a wisdom of the crowd effect is possible, especially by using models of the decision-making processes involved in bidding. The key insight is that, because of the competitive nature of the game, what people bid is not necessarily the same as what they know. This means better estimates are formed by aggregating latent knowledge than by aggregating observed bids. We use our results to highlight the usefulness of models of cognition and decision-making in studying the wisdom of the crowd, which are often approached only from non-psychological statistical perspectives

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments

The evolution and adoption of equity crowdfunding: entrepreneur and investor entry into a new market

Equity crowdfunding (ECF) offers entrepreneurs an online social media marketplace where they can access numerous potential investors who, in exchange for an ownership stake, may supply them with finance. In this paper, we describe the evolution of this market in the UK. Using an inductive qualitative longitudinal research design, we analyse the emerging views of entrepreneurs and investors towards ECF. Our interviewees include large and small-scale investors, as well as market participants who have chosen not to invest or raise funds via ECF. We find that the large financial flows to entrepreneurs in the UK via the ECF platforms, nearly half a billion GBP since 2011, have probably been largely incremental to traditional sources of early stage entrepreneurial finance. Moreover, our research indicates that for the most part, investors appear to understand and appropriately evaluate the risks that they are bearing; ECF investments are perceived as a high risk, high return component within individuals’ portfolios. Investors also use their communication with peers and entrepreneurs via the ECF platform as a learning tool. On the entrepreneurs’ side, ECF allows them to test their products, to develop their brand, to build a loyal customer base and to turn customers into investors. We conclude that policymakers, with the support of a locally appropriate regulatory framework, could support equity crowdfunding as one of the market choices available for entrepreneurs looking to start or grow their ventures

Dynamics of Investor Communication in Equity Crowdfunding

In crowdfunding, start-ups can voluntarily communicate with their investors by posting updates. We investigate whether start-ups strategically use updates, which were previously shown to increase investments. To this end, we use hand-collected data of 751 updates and 39,036 investment decisions from the two major German equity crowdfunding portals Seedmatch and Companisto. We find evidence for strategic communication behavior of startups during an equity crowdfunding campaign. During the funding period, start-ups post updates with linguistic devices that enhance the group identity and the group cohesion. Furthermore, the probability of an update during the funding period increases with a strong competition of other contemporary crowdfunding campaigns