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This paper focuses on the role of managerial cognition as a source of heterogeneity in firm strategies and performance. We link differences in mental models to differences in decision rules and performance in a management simulation. Our results show more accurate mental models lead to better decision rules and higher performance. We also find that decision makers do not need accurate knowledge of the entire business environment; accurate mental models of the key principles are sufficient to achieve superior performance. A fundamental assumption in much of strategic management is that managers who have a richer understanding about organizational capabilities and the dynamics of industry structure can improve the performance of their firms. Our findings provide empirical evidence supporting this assumption and show that differences in mental models help explain ex ante why managers and firms adopt different strategies and achieve different levels of competitive success.

Pairs of disjoint sets (orthopairs) naturally arise or have points in common with many tools to manage uncertainty: rough sets, shadowed sets, version spaces, three-valued logics, etc. Indeed, they can be used to model partial knowledge, borderline cases, consensus, examples and counter-examples pairs. Moreover, generalized versions of orthopairs are the well known theories of Atanassov intuitionistic fuzzy sets and possibility theory and the newly established three-way decision theory. Thus, it is worth studying them on an abstract level in order to outline general properties that can then be casted to the different paradigms they are in connection with. In this paper, we will review how to define orthopairs and a hierarchy on them in the light of granular computing. Aggregation operators will also be discussed as well as possible generalizations and connections with different paradigms. This will permit us to point out new facets of these paradigms and outline some possible future developments.

This research introduces the fully and partially exploratory factor analysis (EFA) with bi-level Bayesian regularization. The proposed models enable factor selection with a sparse model by conceptualizing the factor and loading as the group and individual levels, respectively. They offer a series of benefits such as factor extraction and parameter estimation in one step, simultaneous estimation of the model and tuning parameters, and the availability of interval estimates. Moreover, partial knowledge can be incorporated together with unknown number of factors in the partially EFA. Simulation studies and real-data analyses demonstrated that both models performed satisfactorily under reasonable conditions and were robust to interference of local dependence, while the partially EFA with appropriate information can outperform the fully version and work well under more extreme conditions. The proposed models have been implemented in the R package LAWBL.

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word’s meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word’s meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data.

The beginning of a pandemic is a crucial stage for policymakers. Proper management at this stage can reduce overall health and economical damage. However, knowledge about the pandemic is insufficient. Thus, the use of complex and sophisticated models is challenging. In this study, we propose analytical and stochastic heat spread-based boundaries for the pandemic spread as indicated by the Susceptible-Infected-Recovered (SIR) model. We study the spread of a pandemic on an interaction (social) graph as a diffusion and compared it with the stochastic SIR model. The proposed boundaries are not requiring accurate biological knowledge such as the SIR model does.

Reputation evaluation systems are vital for online platforms, helping users make informed choices based on the trustworthiness of products, services, or individuals. Ensuring privacy and trust in these systems is critical to allow users to provide feedback without fear of retribution or identity exposure. The ring signature (RS), enabling anonymous group-based signing, has garnered attention for building secure and private reputation systems. However, RS-based systems face significant challenges, including the inability to identify malicious users who repeatedly sign the same message, the lack of mechanisms to reveal identities involved in unlawful activities, and a linear growth in signature size with the number of ring members, which poses storage challenges for certain applications. Addressing these limitations, we propose a compressed revocable one-time ring signature (CRORS) scheme leveraging compressible proofs under the Diffie–Hellman Decision and Discrete Logarithm assumptions in the random oracle model. CRORS ensures anonymity, unforgeability, one-time linkability, non-slanderability, and revocability. The one-time linkability feature prevents double-signing, while revocability enables identity disclosure for regulatory enforcement. Additionally, the signature size is reduced to O(logn), significantly enhancing storage efficiency. These improvements make CRORS particularly suitable for blockchain-based reputation systems with ever-growing storage demands. Theoretical analysis validates its effectiveness and practicality.

Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics of the process. In this paper, a new approach is presented for taking advantage of the partial knowledge available about the mechanisms of the process and embedding it into the neural network structure. To validate the proposed approach, it is used to create a forward prediction model for the process of electrochemical micro-machining (μ-ECM). The prediction accuracy of the proposed approach is compared to the prediction accuracy of pure neural structure models with different structures and the results show that the Neural Network (NN) models with embedded knowledge have better prediction accuracy over pure NN models.

Partial knowledge of patient health status and treatment response is a pervasive concern in medical decision making. Clinical practice guidelines (CPGs) make recommendations intended to optimize patient care, but optimization typically is infeasible with partial knowledge. Decision analysis shows that a clinician’s objective, knowledge, and decision criterion should jointly determine the care he prescribes. To demonstrate, this paper studies a common scenario regarding diagnostic testing and treatment. A patient presents to a clinician, who obtains initial evidence on health status. The clinician can prescribe a treatment immediately or he can order a test yielding further evidence that may be useful in predicting treatment response. In the latter case, he prescribes a treatment after observation of the test result. I analyze this scenario in three steps. The first poses a welfare function and characterizes optimal care. The second describes partial knowledge of response to testing and treatment that might realistically be available. The third considers decision criteria. I conclude with reconsideration of clinical practice guidelines.

The current study evaluates guessing behaviors in a vocabulary size test (VST) and examines whether including an I don't know in a VST may have an impact on the results of the test. One-hundred-fifty first-year students at a university in China took part in the study. They were randomly assigned to three groups. Each group took a different version of the VST: the original VST, the VST with an I don't know option, and the VST with an I don't know option and a penalty instruction. After the VST, a reading comprehension test (used as a distraction) and a meaning recall task were administered to all participants. Comparing the results of the VST and the recall task, it was found that guessing behaviors can be influenced by factors such as vocabulary frequency level, partial knowledge, and the multiplechoice options. Results also indicate that the I don't know option not only reduced the number of guesses but also discouraged partial knowledge. Whether to include the I don't know option and the penalty instruction depends on how the test is to be used.

Economists studying public policy have generally assumed that the relevant planner knows how policy affects population behavior. Planners typically do not possess all of this knowledge, so there is reason to consider policy formation with partial knowledge of policy impacts. Here I consider choice of a vaccination policy when a planner has partial knowledge of the effect of vaccination on illness rates. To begin, I pose a planning problem whose objective is to minimize the utilitarian social cost of illness and vaccination. The consequences of candidate vaccination rates depend on the extent to which vaccination prevents illness. I study the planning problem when the planner has partial knowledge of the external-response function, which expresses how the illness rate of unvaccinated persons varies with the vaccination rate. I suppose that the planner observes the illness rate of a study population whose vaccination rate has been chosen previously. He knows that the illness rate of unvaccinated persons weakly decreases as the vaccination rate increases, but he does not know the magnitude of the preventive effect of vaccination. In this setting, I first show how the planner can eliminate dominated vaccination rates and then how he can use the minimax or minimax-regret criterion to choose an undominated vaccination rate.