What do we know and when do we know it?

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a ‘real-world’ setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made

Making monetary policy: what do we know and when do we know it?

President Anthony Santomero points out that conducting a successful monetary policy presents real-world challenges, such as evaluating where the economy is, where it is going, and where it should be going. But how do monetary policymakers make decisions about the economy in a world with imperfect information? Santomero discusses how policymaking is affected by both the availability and reliability of economic information. He concludes that given the information constraints policymakers face, the challenges of setting monetary policy will not go away, so we must find a way to meet themMonetary policy

Oh What a Mistake We Make When We Assume We Know What Our Students Know How To Do…and What To Do About It

This week, making assumptions about what students can and can’t do, and what to do about that.https://digitalcommons.winthrop.edu/weeklyreader/1099/thumbnail.jp

What do we know when we know a compulsive buying person? Looking at now and ahead

Over the last few decades, research has seamlessly confirmed the marked multicausal nature of compulsive buying, since variables from different realms (e.g., family, social, and contextual domains) have demonstrated their explanatory capacity. However, it has been personality variables that have, to a greater extent, aroused the interest of researchers, leading to what is now a cumbersome richness of personal constructs of different nature that seem to require some arrangement under integrative frameworks. The proposal by McAdams under the suggestive title of “What do we know when we know a person?” is, in this regard, especially attractive and thought-provoking. McAdams approaches us to the person as a whole by establishing three differentiated levels (dispositional traits, personal concerns or characteristics adaptations, life story), and it will be precisely these levels that will become the structure we will use to address the state of the art on compulsive buyers. The location of the multiple personal variables analyzed at each of the levels with a common grammar will allow us to identify what it is known, as well as what remains to be done in each space. Lastly, suggestions for the future are given, with particular emphasis on advancing our understanding of the person from each of the academic vantage points but also the suitability of designing studies that integrate and/or build bridges between dispositional traits, characteristic adaptations, and life narratives. The hope is that research in the coming years satisfactorily integrates the different visions of the person to achieve a more comprehensive, nuanced portrait of a person with compulsive buying problemsS

Indicators of regime shifts in ecological systems: what do we need to know and when do we need to know it?

Because novel ecological conditions can cause severe and long-lasting environmental damage with large economic costs, ecologists must identify possible environmental regime shifts and pro-actively guide ecosystem management. As an illustrative example, we apply six potential indicators of impending regime shifts to Carpenter and Brock’s (2006) model of lake eutrophication and analyze whether or not they afford adequate advance warning to enable preventative interventions. Our initial analyses suggest that an indicator based on the high-frequency signal in the spectral density of the time-series provides the best advance warning of a regime shift, even when only incomplete information about underlying system drivers and processes is available. In light of this result, we explore two key factors associated with using indicators to prevent regime shifts. The first key factor is the amount of inertia in the system – how fast the system will react to a change in management, given that a manager can actually control relevant system drivers. If rapid, intensive management is possible, our analyses suggest that an indicator must provide at least 20 years advance warning to reduce the probability of a regime shift to < 5%. As time to, or intensity of, intervention is increased, the necessary amount of advance warning required to avoid a regime shift increases exponentially. The second key factor concerns the amount and type of variability intrinsic to the system, and the impact of this variability on the power of an indicator. Indicators are considered powerful if they detect an impending regime shift with adequate lead time for effective management intervention but not so far in advance that interventions are too costly or unnecessary. Intrinsic “noise” in the system obscures the “signal” provided by all indicators and therefore power of the indicators declines rapidly with increasing within- and between-year variability in measurable variables or parameters. Our results highlight the key role of human decisions in managing ecosystems and the importance of pro-active application of the precautionary principle to avoid regime shifts.Organismic and Evolutionary BiologyOther Research Uni

Decision-Making in Hard Times: What is a Recession, Why Do We Care and When Do We Know We Are in One?

Defining a recessionary event as one which impacts adversely on individuals’ economic well-being, the paper argues that recession is a multi-faceted phenomenon whose meaning differs from person to person as it impacts on their decision-making in real time. It argues that recession is best represented through the calculation of the nowcast of recession event probabilities. A variety of such probabilities are produced using a real-time data set for the US for the period, focusing on the likelihood of various recessionary events through 1986q1-2008q4 and on prospects beyond the end of the sample.Recession, Probability Forecasts, Real Time.

Decision Making in hard Times: What is a Recession, Why Do We Care and How Do We Know When We Are in One?

Defining a recessionary event as one which impacts adversely on individuals’ economic well-being, the paper argues that recession is a multi-faceted phenomenon whose meaning differs from person to person as it impacts on their decision-making in real time. It argues that recession is best represented through the calculation of the nowcast of recession event probabilities. A variety of such probabilities are produced using a real-time data set for the US for the period, focusing on the likelihood of various recessionary events through 1986q1-2008q4 and on prospects beyond the end of the sample.Recession; Probability Forecasts; Real Time

What do we really know about when technological innovation improves performance (and when it does not)?

Most approaches to innovation bear the implicit assumption that increased innovativeness leads to improved organizational performance. Thus, more attention has been focused on innovativeness than on innovation performance; on novelty than on value. However, recent empirical evidence calls into question the unqualified optimism surrounding innovation, and leads us to ask what we really know about when technological innovation improves performance. In this paper, we seek to make a contribution by presenting the results of an exhaustive review of extant knowledge on the outcomes of technological innovation. Our synthesis of the literature allows us to relate in one parsimonious model the drivers and moderators of the antecedents, technical outcomes, and performance outcomes of technological innovation and technological change. We also make sense of the proliferation of terms, and consequent terminological ambiguity, which characterizes a lot of work on technological innovation. Finally, in the light of the model presented and recent developments in work on firm capabilities, we indicate possible avenues for further development of this critical area of research.Technological innovation; organizational performance; innovation and innovativeness;