Unforeseeable Uncertainty Research Articles

Beyond the land of the living death: early-stage transformational entrepreneurs in digital healthcare as liminality navigator

PurposeThe lean startup approach (LSA) is extensively utilized by early-stage entrepreneurs, with “pivot” serving as a key pillar. However, there is a research gap concerning the boundary conditions impacting LSA and pivot decisions, especially when addressing societal challenges, as in the context of transformational entrepreneurship. In this regard, the healthcare sector, further compounded by a lack of research on startups and scale-ups, presents an embraced opportunity to provide multiple contributions for both theory and practice.Design/methodology/approachThe present investigation employs a grounded approach to explore the experiences of the co-founders of a fast-growing Italian e-health startup. A narrative strategy was employed to organize conditions and evolving strategic action/interactions into three different pivoting phases of the startup – before the pivot, its enactment and aftermath – with primary and secondary data collected over a period of one year.FindingsPivoting in digital healthcare unfolded as a liminal experience marked by factors such as high regulation, multiple stakeholders, technological and symbolic ambivalence, resource-intensive demands and institutional actors acting as pathway pioneers, leading to an information overload and unforeseeable uncertainty to manage. These factors challenge entrepreneurs' ability to attain optimal distinctiveness, presenting the paradoxical need for vertical flexibility for scaling up.Social implicationsBy uniquely illuminating the sector’s constraints on entrepreneurial phenomena, this study provides a valuable guide for entrepreneurs and institutional actors in addressing societal challenges.Originality/valueThis study introduces a process model of transformational information crafting when pivoting, highlighting the role of entrepreneurs' transformational stance and platform-mediated solutions as engines behind strategies involving information breaking and transition, preceding knowledge-driven integration strategies.

Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.

Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic

This study introduces a novel index based on expectations concordance for explaining stock-price volatility when novel events that are each somewhat unique cause unforeseeable change and Knightian uncertainty in the process driving outcomes. Expectations concordance measures the degree to which KU events are associated with directionally similar expectations of future returns. Narrative analytics of daily news reports allow for the assessment of bullish versus bearish views in the stock market. Increases in expectations concordance across all KU events results in reinforcing effects and an increase in stock market volatility. Lower expectations concordance produces a stabilizing effect wherein the offsetting views reduce market volatility. The empirical findings hold for ex post and ex ante measures of volatility and for OLS and GARCH estimates.

Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: An application on healthcare and finance

The uncertainty attributed by discrepant data in AI-enabled decisions is a critical challenge in highly regulated domains such as health care and finance. Ambiguity and incompleteness due to missing values in output and input attributes, respectively, is ubiquitous in these domains. It could have an adverse impact on a certain unrepresented set of people in the training data without a developer’s intention to discriminate. The inherently non-numerical nature of categorical attributes than numerical attributes and the presence of incomplete and ambiguous categorical attributes in a dataset increases the uncertainty in decision-making. This paper addresses the challenges in handling categorical attributes as it is not addressed comprehensively in previous research. Three sources of uncertainties in categorical attributes are recognised in this research. The informational uncertainty, unforeseeable uncertainty in the decision task environment, and the uncertainty due to lack of pre-modelling explainability in categorical attributes are addressed in the proposed methodology on maximum likelihood evidential reasoning (MAKER). It can transform and impute incomplete and ambiguous categorical attributes into interpretable numerical features. It utilises a notion of weight and reliability to include subjective expert preference over a piece of evidence and the quality of evidence in a categorical attribute, respectively. The MAKER framework strives to integrate the recognised uncertainties in the transformed input data that allow a model to perceive data limitations during the training regime and acknowledge doubtful predictions by supporting trustworthy pre-modelling and post modelling explainability. The ability to handle uncertainty and its impact on explainability is demonstrated on a real-world healthcare and finance data for different missing data scenarios in three types of AI algorithms: deep-learning, tree-based, and rule-based model.

Una temporalidad en mosaico: nuevas dinámicas del sistema matrimonial y de género en la China urbana contemporánea

Contemporary Chinese society has witnessed ongoing complex institutional and cultural reconfiguration, driven by the transition from the socialist planned economy to marketization and later its deep engagement in globalization and neoliberalism. In this reshaping of Chinese society, tradition and modernity, the resurgence of patriarchal Confucian tradition, the socialist version of modernity, the capitalist version of modernity, and the socialist heritage intermingle, and all seem to define a mosaic temporality.Facing the increasing uncertainties of the market, family members in post-reform China have to stick together as an economic safety net, emotional harbor and spiritual fortress. Chinese parents heavily invest in their children and continue to support them in their adulthood; whereas, the youth is under great obligation to providing old age care for their parents; and parents may regard this heavily-invested offspring as their private product. This resurgent but modified familialism, is not an exact replication of traditional familialism, but is definitely different from the family modes featured by individualism in Western contexts. Mosaic familialism is characterized by a sequential symbiosis between parents and children facing financial constraints and unforeseeable uncertainties with a lack of a social safety net. At the same time, patriarchal Confucianism is rejuvenated to a certain degree, women’s traditional wife and mother role is once again stressed, if not glorified; and the neoliberal discourse articulating personal choice and responsibility, but not individual right, stands by seamlessly. Parents and children, husband and wife are dependent on each other in the intimate family, following a clearly gendered pattern.

Intuitive Ambiguity Management

This paper is about the issue of input parameter uncertainty in portfolio optimization in a discrete setting with finite states (such as the case in a world with different macroeconomic regimes). In such a setting, being unable to assign reliable point estimates to the probabilities (or frequencies) of the states creates the ambiguity. We first describe how this ambiguity can be modeled probabilistically. Then, we show how this added uncertainty can be dealt with in optimal asset allocation problems. In simple-yet-realistic example applications we demonstrate that without sacrificing much of the upside, ambiguity managed portfolios enhance the uniformity of returns across different states when compared to portfolios constructed by traditional methods. We stress that a key advice from these methods builds the case for insurance-like and potentially negative yielding investments such as bonds and commodities so as to hedge the unforeseeable macro uncertainties for a smoother portfolio performance. Finally, we offer a variety of problem domains in which ambiguity management can be nested including macroeconomic scenario based asset allocation, investing with regime switching models, momentum investing, and risk-based investing.

When Project Management Meets Design Theory: Revisiting the Manhattan and Polaris Projects to Characterize ‘Radical Innovation’ and its Managerial Implications

In this paper we propose to revisit two emblematic projects, Manhattan and Polaris, with the models developed by design theory. In particular we demonstrate, relying on C/K theory, how these major projects, traditionally presented as radical innovations, are in fact quite different. In particular we show that the structure of the knowledge base (splitting or non‐splitting) has major consequences. This explains the different managerial strategies of this two cases : whereas Polaris focuses on the control of the design process, Manhattan exhibit a very original strategy, characterized by the simultaneous exploration of different solutions, to manage unforeseeable uncertainties. We discuss the implications of this result for design theory and project management.

Incerteza Imprevisível em Projetos Inovadores: Criando Sentido com a Gestão de Projetos

Quanto maior for o nível de inovação de um projeto, maior a probabilidade de ele conter incerteza imprevisível (unknown unknowns), que deveria ser compreendida pela gestão de riscos. Esse tipo de incerteza pode ser identificadO por sinais precoces de mudanças no ambiente e entendida por meio da criação de sentido (sensemaking). Assim, o objetivo deste artigo é entender como a gestão de projetos cria sentido para as incertezas imprevisíveis em projetos inovadores e se essas práticas são eficientes. Em temos empíricos, foi realizada uma pesquisa de campo que selecionou 16 projetos, totalizando 35 eventos imprevistos representativos de incertezas imprevisíveis. Com base em entrevistas aprofundadas, a análise dos dados foi inicialmente qualitativa e posteriormente foram aplicados testes estatísticos não paramétricos. Observou-se que o método de gestão por aprendizado (experimentação e improvisação) foi mais utilizado nos eventos imprevistos. De fato, a gestão de riscos não foi eficiente para identificar as incertezas imprevisíveis, sendo mais eficientes práticas como coleta de informação e criação coletiva de sentido. Assim sugere-se incorporar um diagnóstico de incertezas, para focar a busca de informações referentes às lacunas de conhecimento em áreas mais incertas do projeto, e socializar o conhecimento de todos os integrantes da equipe e dos stakeholders.

The strategy of parallel approaches in projects with unforeseeable uncertainty: The Manhattan case in retrospect

This paper discusses the literature on the management of projects with unforeseeable uncertainty. Recent work demonstrates that, when confronted with unforeseeable uncertainties, managers can adopt either a learning, trial-and-error-based strategy, or a parallel approach. In the latter, different solutions are developed in parallel and the best one is chosen when enough information becomes available. Studying the case of the Manhattan Project, which historically exemplifies the power of the parallel approach, has led us to show that the either/or logic underlying the existing literature on the parallel approach oversimplifies the question. The Manhattan case demonstrates that managers must not necessarily choose between solutions, but can also combine them or add new ones during the project.

Incentive Contracts in Projects with Unforeseeable Uncertainty

Designing incentive contracts that constructively guide employee efforts is a particularly difficult challenge in novel innovation initiatives, where unforeseen events may occur. Empirical studies have observed a variety of incentive structures in innovation settings: “time and material contracts” (compensation for executing orders), “downside protection” (target‐driven incentives with protection from unexpected risks), and “upside rewards” (additional remuneration for pursuing opportunities). This paper develops a model of incentives in presence of unforeseen events and offers a theoretical prediction of which of the empirically observed incentive structures should be used under which circumstances. The combination of three key influences drives the shape of the best incentive contract. First, the presence of unforeseeable uncertainty, or the occurrence of events that cannot possibly be foreseen at the outset. These may force a change in the project's plan, making pure target setting insufficient. Second, fairness concerns dictate that the employee's expected compensation cannot be shifted downward by unforeseen events, because it would cause demotivation, hostility, and defection. Third, management may not be able to observe the detailed actions of the employee (moral hazard) nor whether a positive or negative unforeseen event has occurred (asymmetric information).

Managing Complexity and Unforeseeable Uncertainty in Startup Companies: An Empirical Study

Novel startup companies often face not only risk, but also unforeseeable uncertainty (the inability to recognize and articulate all relevant variables affecting performance). The literature recognizes that established risk planning methods are very powerful when the nature of risks is well understood, but that they are insufficient for managing unforeseeable uncertainty. For this case, two fundamental approaches have been identified: trial-and-error learning, or actively searching for information and repeatedly changing the goals and course of action as new information emerges, and selectionism, or pursuing several approaches in parallel to see ex post what works best. Based on a sample of 58 startups in Shanghai, we test predictions from prior literature on the circumstances under which selectionism or trial-and-error learning leads to higher performance. We find that the best approach depends on a combination of uncertainty and complexity of the startup: risk planning is sufficient when both are low; trial-and-error learning promises the highest potential when unforeseeable uncertainty is high, and selectionism is preferred when both unforeseeable uncertainty and complexity are high, provided that the choice of the best trial can be delayed until its true market performance can be assessed.

Diagnosing Unforeseeable Uncertainty in a New Venture*

New ventures often do not correctly foresee real market opportunities or the best way to address them. How to cope with unforeseen, unpredictable factors, also referred to as unknown unknowns, is critical for new ventures. Findings in the fields of innovation and project management have shown that dealing with the unpredictable requires management approaches different from those used for classical plan‐and‐achieve‐the‐target projects. Management approaches for novel initiatives include a combination of trial‐and‐error learning (i.e., flexible redefinition of the new venture business model as new information emerges) and selectionism (i.e., running multiple parallel trials and choosing the best performing approach ex post). The management approach must be chosen when the venture is set up. This requires a venture management team to diagnose at the outset whether unknown unknowns are present (or possible), although unknown unknowns cannot be identified initially by definition because they emerge over time. Anecdotal testimony from experienced venture managers and project managers suggests they have a feeling for where their knowledge is limited. However, such a claim is controversial. Some researchers think the concept of diagnosing unforeseeable influence factors is an oxymoron. Thus, the research question in this article is this: How can unforeseeable influence factors in a new venture be diagnosed at the outset? Research to date has insufficiently addressed the a priori identification of the type of uncertainty faced by a new venture. Based on models from decision theory, this article suggests dividing the overall problem of structuring the venture into subproblems for which the management team can identify knowledge gaps. Using a case study, the article describes how knowledge gaps were identified for the subareas of a new venture in a real situation and how this diagnosis was used to correctly identify the areas where unknown unknowns lurk. These areas were managed in a different way (i.e., with learning and experimentation) than the other subproblems (i.e., with targets and deadlines). As a result, the venture could successfully respond to unforeseeable events. The results of this study suggest that a decomposition of the overall venture management problem into subproblems is feasible and natural to managers, that a qualitative assessment of knowledge gaps and vulnerability to unknown unknowns is possible, and that a structured, process‐like approach can be used to identify subproblems, to determine their uncertainty profiles, and to update the uncertainty profiles. These results are immediately useful to venture management and venture capitalists in setting up the venture's structure for effective response to uncertainty. The results advance research about uncertainty management by offering a systematic set of questions for the diagnosis of unknown unknowns before they can be formally described. The usefulness of this process can be tested further in more formal empirical research.

Selectionism and Learning in Projects with Complexity and Unforeseeable Uncertainty

Companies innovating in dynamic environments face the combined challenge of unforeseeable uncertainty (the inability to recognize the relevant influence variables and their functional relationships; thus, events and actions cannot be planned ahead of time) and high complexity (large number of variables and interactions; this leads to difficulty in assessing optimal actions beforehand).There are two fundamental strategies to manage innovation with unforeseeable uncertainty and complexity: trial and error learning and selectionism. Trial and error learning involves a flexible (unplanned) adjustment of the considered actions and targets to new information about the relevant environment as it emerges. Selectionism involves pursuing several approaches independently of one another and picking the best one ex post.Neither strategy nor project management literatures have compared the relative advantages of the two approaches in the presence of unforeseeable uncertainty and complexity. We build a model of a complex project with unforeseeable uncertainty, simulating problem solving as a local search on a rugged landscape. We compare the project payoff performance under trial and error learning and selectionism, based on a priori identifiable project characteristics: whether unforeseeable uncertainty is present, how high the complexity is, and how much trial and error learning and parallel trials cost. We find that if unforeseeable uncertainty is present and the team cannot run trials in a realistic user environment (indicating the project's true market performance), trial and error learning is preferred over selectionism. Moreover, the presence of unforeseeable uncertainty can reverse an established result from computational optimization: Without unforeseeable uncertainty, the optimal number of parallel trials increases in complexity. But with unforeseeable uncertainty, the optimal number of trials might decrease because the unforeseeable factors make the trials less and less informative as complexity grows.

A Formal, Decision-Theoretic Approach to Flexibility and Robustness

Traditional decision-making techniques only deal with a limited type of uncertainty: that which can be foreseen sufficiently to be expressed as a number of alternate moves between which nature will choose. A plan is formulated which specifies how the decision-maker should respond to nature's moves. Such a plan makes no allowance for uncertainty which could not be foreseen. Unforeseeable uncertainty can only be dealt with if the decision-maker's response to nature's moves is not fixed in advance but is itself uncertain. Flexibility is then defined as the entropy of that uncertainty. It is a measure of both the number of alternative sequences of moves which are open to the decision-maker and his attitude to them. Robustness is a way of trading off flexibility against expected value as estimated under foreseeable uncertainty. The cost of flexibility may be estimated and controlled.