RATIONALITY OR IRRATIONALITY? DECISION TO QUIT A JOB TO START A BUSINESS

The paper discusses rationality and decision making with a critical reflection on decisions by employees to quit jobs and set up fragile, micro, and rival businesses. This is a common phenomenon in management consulting businesses in especially emerging economies, where the demand for consultancy services is ballooning. The concepts of decision making and factors that influence decision making were discussed. Rationality and irrationality within the context of economics were briefly defined and theories such as (1) The utility theory , (2) The utility maximization theory and (3)The classical decision theory were used to explain the phenomenon. It was found that reasons for quitting or staying in a management consulting job is not only motivated by preparedness or readiness, rather a complex array of factors, some of which are observable and explainable while others may be attributable to complex cognitive processes that cannot be easily observed and explained. Deciding to leave a job is a complex but intentional rational decision that is influenced by many factors, some of which are observable, environmental, and cognitive. It is in the nature of the economic man to always decide, consequences of which might be sometimes predictable or influenced by events outside of the decision-makers control.

SIMULATION-BASED PERFORMANCE ANALYSIS FOR FUTURE ROBUST MODULAR PRODUCT ARCHITECTURES

The increased demand for customer-adapted product solutions shows an increasing trend of product variety, leading to an increased internal variety and therefore -costs. The concept of modularization provides apossible solution to this challenge by developing modular kits. Nevertheless, modularization methods to not lead to one individual modular kit, but to several alternatives. The decision of which alternative to implement can be crucial to the applying companys succes. During this decision-making both customer- and company perspectives need to be taken into account. This contribution is to present a simulation-based approach to support the decision making by using a model-based configuration system. Furthermore, as classical decision-making processes are based upon historical data, future aspects are usually not taken into account. In order to counteract this situation, this contribution intends to simulate as well future aspects impacting the modular product architecture. In this case, the simulation is used in order to evaluate the individual performances of a Design-for-Variety product architecture as opposed to a Design-for-Future-Robustness by applying this method to the example of customer-individual laser machines.

Prediction oriented behavioral research and its relationship to classical decision research

This paper argues that two of the common methods used in behavioral and social sciences to reduce the chances that models overfit the available data, namely heavy reliance on benchmark models and rigorous parameter estimation techniques, can slow the advancement of these sciences. An examination of classical decision research highlights how applying these methods shaped the field but have also led to limited success. As an alternative, the paper proposes a prediction-oriented approach to the development of behavioral models. Evaluating and comparing models based on their predictive power inherently guards against overfitting and also facilitates accumulation of knowledge. The paper reviews research employing the prediction-oriented approach in behavioral decision research and demonstrates that, in contrast to a common misconception, the focus on predictions can also facilitate better understanding of the underlying processes.

Research and analysis of business management processes implemented under conditions of incomplete certainty

The study carried out in this work made it possible to expand the rank scale for a priori assessment of the chosen strategy in terms of increasing the sensitivity of assessing the caution / negligence ratio using risky, as well as classical decision-making criteria under conditions of statistical uncertainty.

A Novel Multiway Splits Decision Tree for Multiple Types of Data

Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits. Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure. Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive. In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering. This method can combine multiple numerical features, multiple categorical features, or multiple mixed features. Experimental results show that MSDT has excellent performance for multiple types of data.

Judgment as the Imagination of Futures: Practical Rationality in Decisions on Complex Issues

The field of judgment and decision making has seen an explosion of research and analyses since the 1990s, notably in five closely related fields: Rational choice and its variants, the concept of intuition, “dual process” theories, the “heuristics and biases” literature, and the concept of “naturalistic” decision making. Yet none of these theories captures—by design or because of the limits of the approach—the actual mechanism by which emergent judgment occurs on complex decisions. Such decisions are non-optimizable and guided by multiple and often conflicting objectives and values; their outcomes will flow from the nonlinear interaction of many variables whose causal relationships are poorly understood. As a result, critical assumptions of many classical decision making models cannot be met in such situations, and the default approach relies not so much on calculative decision making as on instinctive judgment. This term implies a mechanism that is less calculative and consequentialist that it is imaginative, creative, and unconscious. Emergent, largely intuitive judgment is the only mechanism appropriate to such complex, nonlinear situations in which both an objective maximization of utilities and an accurate assessment of likely consequences are impossible. The concept of judgment broadly defined, as a form of unconscious, emergent, and imaginative interpretation of facts and events, offers the best model for how decision makers approach non-optimizable situations.

Multi-Agent Simulation of Individuals’ Escape in the Urban Rainstorm Context Based on Dynamic Recognition-Primed Decision Model

The urban rainstorm can evolve into a serious emergency, generally characterized by high complexity, uncertainty, and time pressure. It is often difficult for individuals to find the optimal response strategy due to limited information and time constraints. Therefore, the classical decision-making method based on the “infinite rationality” assumption is sometimes challenging to reflect the reality. Based on the recognition-primed decision (RPD) model, a dynamic RPD (D-RPD) model is proposed in this paper. The D-RPD model assumes that decision-makers can gain experience in the escaping process, and the risk perception of rainstorm disasters can be regarded as a Markov process. The experience of recent attempts would contribute more in decision-making. We design the agent according to the D-RPD model, and employ a multi-agent system (MAS) to simulate individuals’ decisions in the context of a rainstorm. Our results show that experience helps individuals to perform better when they escape in the rainstorm. Recency acts as a one of the key elements in escaping decision making. We also find that filling the information gap between individuals and real-time disaster would help individuals to perform well, especially when individuals tend to avoid extreme decisions.

Zasada ostrożności a klasyczne reguły decyzyjne

Zasada ostrożności (ZO) traktowana jest w literaturze przedmiotu jako reguła decyzyjna, która powinna być stosowana w sytuacjach charakteryzujących się niepewnością. Jednakże w ramach teorii decyzji już znacznie wcześniej wypracowano algorytmy postępowania w warunkach niepewności. Celem niniejszego artykułu jest porównanie klasycznych reguł decyzyjnych z zasadą ostrożności i odpowiedź na pytanie: czy zasadę ostrożności można zredukować do którejś z klasycznych reguł. Okazuje się, że taka redukcja nie jest możliwa. Precautionary Principle vs Formal Decision: Making Criteria Precautionary principle (PP) is commonly understood as a criterion that should be used in decision-making under risk and/or uncertainty. Yet, long before the approval of PP in the literature – a set of concrete, formal criterions existed in the field of decision theory. Therefore the main goal of this paper is to compare these classical decision – rules with PP. This very comparison will bring us to answer the question: if PP may be reduced to any of the classical criterions? It will tur out that such a reduction cannot be done. Therefore PP has got its own specificity.