Decision Making Under Uncertainty

The paper focuses on the theoretical determination of the zone of uncertainty, the reasons for its origin and the ways to overcome it in the decision-making process on the use of security forces. In this decision-making process, the security forces leaders are constantly under certain temporal, social, economic or other constraints. Furthermore, an aggravating circumstance is the lack of information on necessary events that are most often manifested as the zones of uncertainty.

Lo studio si propone di analizzare l'importanza che le emozioni assumono all'interno della scelta intertemporale, ovvero all'interno di una scelta che si trova a dover decidere se preferire una gratificazione presente ed immediata o un vantaggio maggiore in un futuro più o meno distante. Il focus della trattattazione sono il concetto di autocontrollo e l'emozione complessa del riampianto.

With the deepening penetration of renewable resources worldwide, power system operators are faced with emerging challenges, e.g., the increase of operating risks due to the volatility and uncertainty of wind and solar power. To efficiently identify the operational limit violations, a switch from deterministic to stochastic framework for assessing the system security, which could manage various types of uncertainties, has been advocated in this paper. The established model is based on an improved probabilistic load flow, which is adapted to incorporate the steady-state behavior of frequency regulation. An efficient importance sampling (IS) technique is also developed to speed up the crude Monte Carlo (MC) simulation in estimating the low probability of violations of security constraints. Extensive computational experiments on both the IEEE 14-bus test case and a simplified regional system show that the proposed IS estimator makes significant enhancement to the crude MC in the computational efficiency and has better numerical performance as compared with other IS schemes.

Simulated Annealing (SA) is a reasonable algorithm for solving optimization problems, through the selection of the best solution among a finite number of possible solutions. It is a particularly attractive technique to solve fuzzy optimization problems, because it allows finding near-optimal solutions, which, in a fuzzy environment, is usually good enough and without a large computational effort. We present a representative set of problems for testing the SA algorithm suitability and performance. The SA performance is measured in terms of the objective function values, considering several trade-offs on constraints satisfaction levels, and computational time to achieve a solution. Furthermore, we discuss the parameters that control the SA algorithm to show how easily they can be manipulated. The set of fuzzy optimization problems tested were formulated following the complete fuzzification method proposed by Ribeiro and Moura-Pires (1999). The results obtained show the flexibility and adaptability of the SA algorithm to solve fuzzy optimization problems.

In this paper, we investigated how justifications for price increases are associated with risky decision making and emotional responses. Across two studies with paired lottery choices and sequential decisions, we found that participants presented with a justification for price increases based on increasing demand decided to invest in a comparatively riskier asset more often than participants presented with a justification for price increases based on increasing tax or those presented with no justification at all. We also found that participants presented with justifications for price increases based on increasing demand also reported higher arousal and displayed higher galvanic skin response than people in the other two justification conditions. Together, these studies provide evidence that only the increasing demand condition underlying a price increase of a risky asset can influence the decision to buy and suggests that emotional activation has a crucial role in such a decision process.

Fault tree analysis is a known methodology used for analysing engineering systems. The approach is usually conducted
using known failure data. Given that most maritime operations are conducted in a challenging and uncertain environment,
their failure data are usually unavailable requiring a flexible and yet robust algorithm for the analysis of the systems.
This research therefore seeks to analyse hazards of ships during ship and port interface operations as a result of manoeuvring
by incorporating fuzzy fault tree analysis method to optimise the performance effectiveness of the system.
Fuzzy set theory provides the needed flexibility to represent vague information from the analysis process. The methodology
is structured in such a manner that diverse sets of data can be integrated and synthesised for analysing the system.
It is envisaged that the proposed method could avail safety specialist with a simple and a step-by-step framework
for evaluating maritime hazards in seaport operations in a concise way.

Prediction of fuel consumption rate level of a vessel per voyage posed to be a challenge under uncertainties. In such uncertain conditions, revealing of fuel consumption rate levels of the fleet of vessels is deemed imperative to ensure effective and efficient operations of the vessels per voyage. Therefore, development of uncertainty treatment model is necessary in this research. A combined algorithm that is made up of fuzzy rule base and utility theory methods is incorporated in the investigation of the fuel consumption rate levels of marine vessels. The mechanism of the algorithm is used to capture and combine all the important parameters that determine the fuel consumption rate level of each marine vessel. The workability of the model is demonstrated. The produced results revealed that the combined algorithm can support estimation of the fuel consumption rate levels of marine vessels and show which vessel is better than the other. Based on the results of this article, the management and crew of a marine vessel are equipped with the necessary decision support tool to optimize, implement and manage improvements on the performance characteristics and fuel consumption rate of their marine vessels during voyages.

Liquefied natural gas (LNG) transfer arms have been in regular operations due to rapid increase in the demand of an LNG. The LNG transfer arms are used in loading and unloading of the LNG to ships and onshore tanks. Best safety practises need to be put in place for effective operations of the transfer arms. The cost of carrying out such exercise must be considered for industrial benefit. Therefore, an investigation of the cost of implementation of safety improvement measures (SIMs) for optimal operations of an LNG transfer arm need to be carried out under uncertainty. Such SIMs can be used in protection of the environment and ship crew members. A model that is made of a hybridised algorithm such as fuzzy logic and evidential reasoning (ER) is employed for the exercise. The model is used to capture all the necessary information that is needed in addressing uncertainties associated with the costs of application of the SIMs. The mechanisms of the ER algorithm such as aggregation of multiple ideas to a single level are incorporated into intelligent decision system software to facilitate identification of a SIM that can optimise the LNG transfer arm operations in a cost effective manner.

In this research, the safety/risk level of a liquefied natural gas (LNG) carrier system is investigated through the application of a fuzzy evidential reasoning (FER) method to uncertainty treatment of its failure modes. A fuzzy set manipulation formula with multiple parameters such as consequence severity, failure consequence probability, and failure likelihood is used to assess the safety/risk levels of failure modes of the system. Such failure estimations are synthesized to evaluate the whole system's safety/risk level using an evidential reasoning (ER) approach. Risk control options (RCOs) are developed for reduction/control of high-risk level of the system. The best RCO, which is the one with the highest preference degree, is used to improve the safety level of the system.

The purpose of this review is to communicate the research results of Chinese academia in this field to interested scholars in Western academia. Our review question is: What were the factors that positively or negatively influenced China's export? We designed a systematic review protocol and then conducted a seven-round in-depth search. After the searching and checking process, a database of 620 academic journal articles was created. Altogether, we find 269 factors have boosted or hindered China's export. Majority of researches focused on macro level factors. Very few papers studied the firm level or individual level factors. Based on our database, several most frequently studied factors that affect China's export are identified. In the end, a future research direction is proposed.

A good estimation of consumers' expected response to specific policy measures is of paramount importance in the design of effective schemes for the adoption of new technologies. The decision-making process of consumers is influenced by a multitude of factors. In this context, agent-based modeling techniques provide an appropriate framework to model such private adoption decisions. However, the models currently in use often fail to capture uncertainties related to agency and their ability to replicate reality. In order to address this drawback, we developed an agent-based technology adoption model that is supported by a complete framework for parameter estimation and uncertainty quantification based on historical data and observations. The novelty of our model lies in obtaining realistic uncertainty bounds and splitting the total model output uncertainty in its major contributing uncertainty sources. In this article, we demonstrated its applicability by exploring the evolution of the market share of small-scale PV systems in Greece, under two support schemes of interest. Our results indicated that, over 2018-2025, the net-metering scheme, currently operational, seems more effective than a proposed self-consumption scheme that subsidizes residential storage by 25%; however, the former scheme's effectiveness is mainly related to the retail price of electricity. They also highlighted that storage investment costs need to follow a steep learning curve of at least a 10% annual reduction until 2025, for self-consumption to become attractive to consumers in Greece. Nevertheless, simulations showed that none of these two schemes can be as efficient as the previous feed-in-tariffs scheme.

Managers face a critical issue in deciding when to employ a predictive planning approach versus a more adaptive and flexible strategic approach. We suggest that determining which approach is ideal for a given context hangs on the extent to which uncertainty is, or might be, mitigable within that context. To date, however, the mitigability of uncertainty has not been adequately distilled. Here we take on this issue, distinguishing mitigable ignorance of pertinent but knowable information (i.e. epistemic uncertainty) from immitigable indeterminacy (i.e. aleatory uncertainty). We review the current state of the debate on the existence of free will because the acceptance or rejection of conscious agents as a true first cause has fundamental implications. A critical examination of the arguments for and against the free will hypothesis land us on the side of voluntarism, which implies immitigable indeterminacy (but not complete unpredictability) wherever conscious actors are involved. Accepting the existence of immitigable or aleatory uncertainty, then, we revisit the determination of strategic logics and produce important theoretical nuance and key boundary conditions in the normative choice between predictive and non-predictive strategies.

Crises come in different forms and certain challenges that may be considered as crises need to be addressed by a combination of activities by multiple parties over a longer-time period than past emergencies that could be quickly tackled by crisis management teams. Their implications are such as to require more than “business as usual responses”. Compared with normal levels of change, uncertainty and time pressure, crisis situations can make different demands upon board members. Some directors may be better equipped to handle them than others. Many boards need to review their strategies, policies and practices for managing existential risks and crises, and review their sources of crisis counsel. There are various questions that should be considered when undertaking such as review. Speech delivered in plenary session at the 2019 Annual Conclave and 29th Annual Day of India’s Institute of Directors (IOD). 

Published as: Coulson-Thomas, Colin (2019), Boards Strategies for Navigating Crises in a Volatile World (Speech at 2019 Annual Conclave and 29th Annual Day of India’s IOD), https://www.ims-productivity.com/  [https://www.ims-productivity.com/page.cfm/content/Annual-Directors-Conclave-and-29th-IOD-Annual-Day, 10th September]

State-dependent utility is a problem for the behavioral branch of decision theory under uncertainty. It questions the very possibility that beliefs be revealed by choice data. According to the current literature, all models of beliefs are equally exposed to the problem. Moreover, the problem is solvable only when the decision-maker can influence the resolution of uncertainty. This paper gives grounds to reject these two views. The various models of beliefs can be shown to be unequally exposed to the problem of state-dependent utility. The problem can be argued to be solved even when the decision-maker has no influence over the resolution of uncertainty. The implications of such reappraisal for a philosophical appreciation of the revealed preference methodology are discussed.

In this paper, I investigate the betting behavior of a decision-maker who can influence the likelihood of the events upon which she is betting. In decision theory, this is best known as a situation of moral hazard. Focusing on a particularly simple case, I sketch the first systematic analysis of moral hazard in the canonical Savage framework. From the results of this analysis, I draw two philosophical conclusions. First, from an observational and a descriptive point of view, there need to be no incompatibility between moral hazard and the Savage framework. This qualifies the incompatibility view, that is ubiquitous in decision theory. Second, in general, moral hazard is not sufficient to overcome the challenges posed by state-dependent utility to the behavioral identification of beliefs. This qualifies the sufficiency view, that is influential in decision theory. These two philosophical conclusions are the main contributions of my paper.

This paper examines the preference-based approach to the identification of beliefs. It focuses on the main problem to which this approach is exposed, namely that of state-dependent utility. First, the problem is illustrated in full detail. Four types of state-dependent utility issues are distinguished. Second, a comprehensive strategy for identifying beliefs under state-dependent utility is presented and discussed. For the problem to be solved following this strategy, however, preferences need to extend beyond choices. We claim that this a necessary feature of any complete solution to the problem of state-dependent utility. We also argue that this is the main conceptual lesson to draw from it. We show that this lesson is of interest to both economists and philosophers.