Unknown Unknowns Research Articles

The Known Unknowns: Missing Pieces in in vivo Models of Fragile X Syndrome

The Known Unknowns: Missing Pieces in in vivo Models of Fragile X Syndrome

Editor's Introduction

Editor's Introduction Arien Mack the papers in this special issue of social research were first presented on April 4 and 5, 2019, at the thirty-eighth Social Research conference at the New School, "Unknowability: How Do We Know What Cannot Be Known?" From the earliest moments of humanity's search for answers and explanations, we have grappled with the unknowable—that which we are unable, or not permitted, to know. In this issue, our authors explore how we decide that something is unknowable rather than merely not yet known, and what criteria we use in determining that some knowledge is beyond our reach, and whether these criteria differ across fields and disciplines. What does the history of the unknowable look like? What are the now-answered questions that we once thought unanswerable? Are there enduring unknowables? Are there modes of understanding that are different from the ones used by scientists, and what is the status of knowledge gained in these alternate ways? These and other questions were the subject of our conference and are addressed in this issue. The question of what we cannot know is not only important in its own right but has also taken on additional importance in light of the rise of misinformation and "alternative facts." A better understanding of whether or not something can possibly be known has the capacity to shape inquiry, scholarly research, and public education. Furthermore, identifying what kinds of questions are unanswerable is of great intellectual and perhaps even political significance—to wit, Donald Rumsfeld's now-famous statement that "there are known knowns … there are known unknowns … and there are also unknown unknowns," to which Fintan O'Toole added "unknown knowns." [End Page xxxi] The "Unknowability" conference afforded a rare opportunity for scholars from different fields to engage with these questions during this seemingly post-truth moment in which we all are living. We hope the conference papers collected in this special issue will encourage our readers to explore these questions themselves. We are grateful to the National Science Foundation for providing the funding that made the "Unknowability" conference possible. [End Page xxxii] Copyright © 2020 The New School

Investigation of the chemical compounds in Pheretima aspergillum (E. Perrier) using a combination of mass spectral molecular networking and unsupervised substructure annotation topic modeling together with in silico fragmentation prediction

Untargeted mass spectrometry analysis is one of the most challenging and meaningful steps in the rapid structural elucidation of the highly complex and diverse constituents of traditional Chinese medicine. Specifically, it is a laborious and time-consuming way to identify unknown compounds. Herein, a workflow was proposed to expedite the annotations of the chemical structures in Pheretima aspergillum (E. Perrier) (Di-Long, DL). First, ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS) was performed to obtain the untargeted mass spectral data. Then, the spectral data were uploaded to the Global Natural Products Social Molecular Networking (GNPS) platform to create a network and extract the Mass2Motifs (co-occurring fragments and neutral losses) using unsupervised substructure annotation topic modeling (MS2LDA). Finally, a structural analysis was performed using the proposed workflow of MS2LDA in combination with mass spectral molecular networking and in silico fragmentation prediction. As a result, a total of 124 compounds from DL were effectively characterized, of which 89 (7 furan sulfonic acids, 57 phospholipids and 25 carboxamides) were identified as potentially new compounds from DL. The results presented in this article significantly improve the understanding of the chemical composition of DL and provide a solid scientific basis for the future study of the quality control, underlying pharmacology and mechanism of DL. Moreover, the proposed workflow was used for the first time to accelerate the annotations of unknown molecules from TCM. Furthermore, this workflow will increase the efficiency of characterizing the ‘unknown knowns’ and elucidation of the ‘unknown unknowns’ from TCM, which are crucial steps of discovering the natural product drugs in TCM.

Structural robustness: A revisit

The growing need for assuring efficient and sustainable investments in civil engineering structures has determined a renovated interest in the rational design of such structures from designers, clients and authorities. As a result, risk-informed decision-making methodologies are increasingly being used as a direct decision tool or as an upper-level layer from which performance-based approaches are then calibrated against. One of the most important and challenging aspects of today's structural design is to adequately handle the system-level effects, the known unknowns and the unknown unknowns. These aspects revolve around assessing and evaluating relevant damage scenarios, namely those involving unacceptable/intolerable damage levels. Hence, the importance of risk analysis of disproportionate collapse, and along with it of robustness. However, the way robustness has been used in modern design codes varies substantially, from simple provisions of prescriptive rules to complex risk analysis of the disproportionate collapse. As a result, implementing design for robustness is still very much a grey area and more so when it comes to defining means to quantify robustness. This paper revisits the most common robustness frameworks, highlighting their merits and limitations, and identifies one among them which is very promising as a way forward to solve the still open challenges.

Taming the Unknown Unknowns in Complex Systems: Challenges and Opportunities for Modeling, Analysis and Control of Complex (Biological) Collectives.

Despite significant effort on understanding complex biological systems, we lack a unified theory for modeling, inference, analysis, and efficient control of their dynamics in uncertain environments. These problems are made even more challenging when considering that only limited and noisy information is accessible for modeling, which can prove insufficient for explaining, and predicting the behavior of complex systems. For instance, missing information hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the model structure and parameters of complex biological systems. Toward this end, in this paper, we discuss several important mathematical challenges that could open new theoretical avenues in studying complex systems: (1) By understanding the universal laws characterizing the asymmetric statistics of magnitude increments and the complex space-time interdependency within one process and across many processes, we can develop a class of compact yet accurate mathematical models capable to potentially providing higher degree of predictability, and more efficient control strategies. (2) In order to better predict the onset of disease and their root cause, as well as potentially discover more efficient quality-of-life (QoL)-control strategies, we need to develop mathematical strategies that not only are capable to discover causal interactions and their corresponding mathematical expressions for space and time operators acting on biological processes, but also mathematical and algorithmic techniques to identify the number of unknown unknowns (UUs) and their interdependency with the observed variables. (3) Lastly, to improve the QoL of control strategies when facing intra- and inter-patient variability, the focus should not only be on specific values and ranges for biological processes, but also on optimizing/controlling knob variables that enforce a specific spatiotemporal multifractal behavior that corresponds to an initial healthy (patient specific) behavior. All in all, the modeling, analysis and control of complex biological collective systems requires a deeper understanding of the multifractal properties of high dimensional heterogeneous and noisy data streams and new algorithmic tools that exploit geometric, statistical physics, and information theoretic concepts to deal with these data challenges.

Brexit and ‘Unknown Unknowns’

Brexit and ‘Unknown Unknowns’

Testosterone: the known knowns, the known unknowns and the unknown unknowns

Testosterone: the known knowns, the known unknowns and the unknown unknowns

Unknown Knowns: Michael Haneke's Caché and the Failure of Allegory

Michael Haneke's 2005 film Caché uses the police massacre of North African demonstrators that took place in Paris on 17 October 1961 as an allegory of national guilt. This article considers the ethical stakes of this formal move, arguing that the problem of concealment at the heart of the film's narrative is also the reason why the 1961 massacre does not ‘work’ as an allegory. In this failure, Caché reveals a dilemma unique to traumatic histories that have escaped widespread recognition: the tension between the absence of collective memory and the potentially reparative capacities of elliptical representation.

Known Unknowns and Unknown Knowns: What we know about the cannabis and the Hashish trade in Afghanistan

In the past four decades, much of the contemporary narrative of Afghanistan has been defined by opium. However, underneath the veil of the opium economy, the cannabis trade remains an enduring component of Afghanistan’s political economy and culture. Much of this stems from the long history of cannabis cultivation and hashish production in the region. During the 1960s, the growing demand from Western nations for Afghan hashish helped forge key global trafficking networks, as well as significant changes to the cultivation of cannabis and production of hashish. Since then, production and trade evolved, with cultivation of cannabis more widespread. Ultimately, analyzing the cannabis trade and its historical antecedents, reveals how the cannabis trade, like the opium economy, transformed in response to local, regional, and global factors, remaining an important piece of the rural Afghan economy today.

A Coverage-Based Utility Model for Identifying Unknown Unknowns

A classifier’s low confidence in prediction is often indicative of whether its prediction will be wrong; in this case, inputs are called known unknowns. In contrast, unknown unknowns (UUs) are inputs on which a classifier makes a high confidence mistake. Identifying UUs is especially important in safety-critical domains like medicine (diagnosis) and law (recidivism prediction). Previous work by Lakkaraju et al. (2017) on identifying unknown unknowns assumes that the utility of each revealed UU is independent of the others, rather than considering the set holistically. While this assumption yields an efficient discovery algorithm, we argue that it produces an incomplete understanding of the classifier’s limitations. In response, this paper proposes a new class of utility models that rewards how well the discovered UUs cover (or "explain") a sample distribution of expected queries. Although choosing an optimal cover is intractable, even if the UUs were known, our utility model is monotone submodular, affording a greedy discovery strategy. Experimental results on four datasets show that our method outperforms bandit-based approaches and achieves within 60.9% utility of an omniscient, tractable upper bound.

Embracing Enrichment and Unknown Unknowns*

Embracing Enrichment and Unknown Unknowns*

Unknown Knowns: The Past, Present, and Future of Graduate Preparation for Two-Year College English Faculty

Intended to contextualize and elaborate on the Two-Year College English Association's 2016 Guidelines for Preparing Teachers of English in the Two-Year College, this article examines the history, current status, and possible futures of graduate preparation for two-year-college English professionals. It traces the five-decade history of efforts among two-year-college English faculty to articulate the distinct demands and opportunities of their profession and to hold university-based graduate programs accountable for providing meaningful preparation for future two-year- college teacher-scholars. Based on our survey of this history and the current landscape of graduate education in English studies, we argue that transforming graduate programs to meet the needs of the teaching majority will require embracing the four principles articulated in TYCA's 2016 Guidelines: develop curricula relevant to two-year-college teaching; collaborate with two-year-college colleagues; prepare future two-year-college faculty to be engaged professionals; and make two-year colleges visible to all graduate students.

Cas9 in action: no more known unknowns?

Useful new methods are being introduced to experimentally determine the genome-wide consequences of Cas9-based editing.

Resolve the Genome’s Known Unknowns

Resolve the Genome’s Known Unknowns

The Use of Simulation to Reduce the Domain of "Black Swans" with Application to Hurricane Impacts to Power Systems.

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.

Unknown Knowns and Known Unknowns: Framing the Role of Organizational Learning in Corporate Social Responsibility Development

AbstractCorporate social responsibility (CSR) is now widely seen as an increasingly significant concern for firms because of moral, relational and instrumental motives. Nevertheless, practical aspects and challenges associated with CSR development in firms remains only partially understood. In this setting, the organizational learning (OL) discipline is recurrently put forward as key in the pursuit and successful development of CSR, but the existing literature remains disjointed. This study critically reviews the existing literature to conceptualize how research to date has approached CSR development in terms of OL, and to provide a two‐dimensional structuring framework of the role of OL in CSR development that emphasizes key OL‐related aspects supporting CSR development and goes beyond an organization‐centric viewpoint to consider not only learning within the organization, but also from others, and with others. In particular, the authors identify key learning processes and sub‐processes and critical areas that remain understudied. Overall, the authors propose a macro view of the work done to date at the intersection of OL and CSR, and in doing so help make the ‘OL for CSR development’ scholarship more recognizable as a sub‐discipline.

What cliff is your business headed for? Are you listening and are the right people talking?

Purpose This paper addresses one particular type of cliff edge – “Unknown Knowns” that organizations are often headed for and suggests ways in which it can be avoided. Design/methodology/approach The paper reports lessons learned through the author’s experience and draws on relevant literature. Findings Apparently unforeseen cliffs are actually well known about, deep in the organization, well ahead of time and either nobody is talking or nobody is listening. Practical Implications People need to change their cultures to ensure that the “Cassandras” (people who can be seen as “negative” and problem rather than solution focused) are genuinely listened to. Originality/value New scandals break every month. At the time of writing, the latest is Volkswagen (VW) emissions testing, itself a classic example of an unknown-known cliff.

Risk analysis and Black Swans: two strategies for de-blackening

With his book The Black Swan: The Impact of the Highly Improbable, Nassim N. Taleb brought the Black Swan metaphor to the center of risk analysis debates. His main thesis is that the course of the world is determined by the sudden coming of a small number of high impact events whose occurrences are detectable only through hindsight. When relying on prediction, risk analysis is doomed to fail. The standard route of refutation and rehabilitation of predictive practices has been to oppose Taleb’s worldview. In this article, we develop an alternative route of refutation in which we separate Taleb’s claim of unpredictability from his worldview. Moreover, in our justification of the possibilities of predicting in a Black Swan world, we favor an extension of the Black Swan domain so that it includes unknown knowns in addition to unknown unknowns. The article advocates for a bridging of perspectives in which predicting not only involves the sphere of knowledge, but also the sphere of imagination, bringing the risk communication to the fore. Related to the analysis of extraordinary high-impact events, the merits of an imaginative perspective on prediction is illustrated by the terrorist attacks in Norway on July 22 2011, and some key challenges for further developing such a perspective within risk analysis are sketched.

Managing Project Uncertainty: The Delphi Method

Management Decision making in uncertain environments is key to the successful delivery of oil and gas projects. By definition, however, uncertainty is ambiguous and unpredictable. Uncertainty does not necessarily imply risk; the two are separate concepts. Economist Frank Knight made a clear distinction between the two as far back as 1921. Risk can be measured and quantified and is well suited to the analytical techniques used in project risk management; uncertainty defies quantitative expression. Items with the quality of uncertainty often surface during risk-assessment exercises but tend to be categorized as “issues” and are neither captured nor addressed by traditional risk-management processes. Donald Rumsfeld’s infamous quote highlights the difficulty of articulating uncertainty. However, the statement is rooted in sound cognitive and sociological theory and makes an important point about the perils of taking our individual and shared perceptions for reality. The philosopher Slavoj Žižek (2006) suggests that there may also be a fourth category of “unknown knowns”—those things that we intentionally refuse to acknowledge due to, for example, the social stigma around dissent. While we may feel confident dealing with “known knowns” and “known unknowns,” how can we hope to discover the “unknown unknowns” and uncover the “unknown knowns”? These problems can be effectively addressed using a Delphi-based workshop methodology. The process presented in this article discusses how to work collaboratively to develop a common understanding of a project and achieve alignment on issues and uncertainties. This allows managers to gain a better understanding of the critical uncertainties running through the project and implement appropriate strategies. Delphi Methods Delphi is a structured communication technique that was developed as part of the United States’ response to global uncertainty during the Cold War. Since then, the process has been used across many different industries worldwide (Linstone and Turoff 2002). Delphi-based methods are able to draw out the tacit knowledge held by project members and assess it in a structured manner.

Emerging risk – Conceptual definition and a relation to black swan type of events

The concept of emerging risk has gained increasing attention in recent years. The term has an intuitive appeal and meaning but a consistent and agreed definition is missing. We perform an in-depth analysis of this concept, in particular its relation to black swan type of events, and show that these can be considered meaningful and complementary concepts by relating emerging risk to known unknowns and black swans to unknown knowns, unknown unknowns and a subset of known knowns. The former is consistent with saying that we face emerging risk related to an activity when the background knowledge is weak but contains indications/justified beliefs that a new type of event (new in the context of that activity) could occur in the future and potentially have severe consequences to something humans value. The weak background knowledge among other things results in difficulty specifying consequences and possibly also in fully specifying the event itself; i.e. in difficulty specifying scenarios. Here knowledge becomes the key concept for both emerging risk and black swan type of events, allowing for taking into consideration time dynamics since knowledge develops over time. Some implications of our findings in terms of risk assessment and risk management are pointed out.