Brain areas activated by uncertain reward-based decision-making in healthy volunteers

Reward-based decision-making has been found to activate several brain areas, including the ven- trolateral prefronta~ lobe, orbitofrontal cortex, anterior cingulate cortex, ventral striatum, and mesolimbic dopaminergic system. In this study, we observed brain areas activated under three de- grees of uncertainty in a reward-based decision-making task （certain, risky, and ambiguous）. The tasks were presented using a brain function audiovisual stimulation system. We conducted brain scans of 15 healthy volunteers using a 3.0T magnetic resonance scanner. We used SPM8 to ana- lyze the location and intensity of activation during the reward-based decision-making task, with re- spect to the three conditions. We found that the orbitofrontal cortex was activated in the certain reward condition, while the prefrontal cortex, precentral gyrus, occipital visual cortex, inferior parietal lobe, cerebellar posterior lobe, middle temporal gyrus, inferior temporal gyrus, limbic lobe, and midbrain were activated during the ＇risk＇ condition. The prefrontal cortex, temporal pole, inferior temporal gyrus, occipital visual cortex, and cerebellar posterior lobe were activated during am- biguous decision-making. The ventrolateral prefrontal lobe, frontal pole of the prefrontal lobe, orbi- tofrontal cortex, precentral gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus, infe- rior parietal Iobule, and cerebellar posterior lobe exhibited greater activation in the ＇risk＇ than in the ＇certain＇ condition （P 〈 0.05）. The frontal pole and dorsolateral region of the prefrontal lobe, as well as the cerebellar posterior lobe, showed significantly greater activation in the ＇ambiguous＇ condition compared to the ＇risk＇ condition （P 〈 0.05）. The prefrontal lobe, occipital lobe, parietal lobe, temporal lobe, limbic lobe, midbrain, and posterior lobe of the cerebellum were activated during deci- sion-making about uncertain rewards. Thus, we observed different levels and regions of activation for different types of reward processing during decision-making. Specifically, when the degree of reward uncertainty increased, the number of activated brain areas increased, including greater ac- tivation of brain areas associated with loss.

A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

This paper presents an operational framework of unstructured decision-making approach involving quality function deployment(QFD)in an uncertain linguistic context.Firstly,QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment.Secondly,hesitant fuzzy linguistic term sets(HFLTSs),which facilitate the management and handling of information equivocality,are designed to construct a house of quality(HoQ)in the product planning process.The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets.Thirdly,a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization.The inter-relationships of cooperative partners are directly matched with a back propagation neural network(BPNN)to construct the multi-enterprise manufacturing network.The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives.Finally,a real-world example,namely,the prototype manufacturing of an automatic transmission for a vehicle,is provided to illustrate the effectiveness of the proposed decision-making approach.

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

A stochastic approach for integrating market and technical uncertainties in economic evaluations of petroleum development

The paper presents a stochastic and economic analysis for petroleum development under uncertain market and technical environments. Mean-reversion with jumps for price forecasting is used to consider market uncertainty, while various scenarios for the reservoir properties and cost are employed to consider technical uncertainty. Monte Carlo simulation is carried out to obtain the feasible range of net present values and internal rates of return. The influence of stochastic parameters is examined through correlation coefficients. The stochastic approach yields more reliable evaluation and effectively investigates the characteristics of development. The integration of uncertainties and contractual terms results in an irregular tendency in the future cash flow and reveals that a larger reserve does not guarantee a greater profit. The reserve and the well rate affect the economic values whereas the parameters for price prediction don＇t. The research confirms the necessity of qualifying uncertainties for realistic decision-making at the initial stage of development.

Decision-making under uncertainty for buildings exposed to environmental hazards

Buildings are exposed to risks from environmental hazards such as earthquakes,windstorms and floods.Sub-stantial uncertainties from various sources are inevitably involved in the risk estimation and decision-making for activities such as design and disaster risk mitigation for buildings.Decision makers seek to achieve economic efficiency while ensure building safety by managing the extreme tail risk that is typically a concern when facing low-probability,high-consequence events.Thus,risk preferences and tolerances play an important role in the decision process,which often vary among different decision makers.The conventionally used minimum expected life-cycle cost criterion(MELC)fails to adequately cope with large uncertainty and risk preferences.To this end,this paper presents the application of a set of decision models beyond the MELC to support decision-making under uncertainty for buildings exposed to environmental hazards.The objective is to provide risk-informed de-cision support for decision-makers with a wide range of risk appetites while taking into account uncertainties involved in the life-cycle cost.The features,strengths and weaknesses of these decision models are discussed from a practical point of view.The application and selection of the decision models are demonstrated by two practical decision problems:(i)seismic design of a high-rise commercial building,and(ii)wind hazard mitigation for a low-rise residential building.These examples illustrate how the decisions for choosing seismic design levels and wind mitigation measures vary when different decision models and model settings are applied.

A method for emergency response alternative decision-making under uncertainty

In this paper,an emergency decision-making method,based on case-based reasoning and cloud model,is proposed to solve the risk decision-making problem in emergency response.Casebased reasoning,by allowing the decision-maker to referring to past decisions,introduces a short-cut to formulate feasible emergency alternatives.Cloud model is used to evaluate and optimise the emergency response alternatives.To evaluate emergency response alternatives,the decision criterion must be determined according to the aim and characteristics of emergency rescue in disasters or accidents.Then,the weight cloud and evaluation cloud of the decision criterion are determined by the Delphi method combined with backward cloud generator,and the synthesised cloud of each alternative is calculated through arithmetic rules of cloud.Finally,a ranking of all response alternatives can be determined,and the best alternative is selected.Case study shows that the method makes the conversion between qualitative description and quantitative indication more effective.

A FUZZY DECISION-MAKING TECHNIQUE WITH TWO KINDS OF UNCERTAINTY

A novel fuzzy decision-making technique concerning two kinds of uncertainty is intro-duced. It uses a similarity measure of fuzzy sets and threshold λ to determine whether arule should be fired and a modification function (MF) is used to modify the deduced con-sequent. The strength of confirmation (SC) of every production rule, which is given by ex-perts, is distinctive and is used to modify the consequent once more. It provides a more effi-cient tool for the building of some Expert Systems. Finally, an efficient algorithm is pro-posed and numerical examples are presented.

Risk assessment:method and case study for traffic projects

A comprehensive project evaluation and decision-making method considering multiple objectives,stakeholders,and attributes of proposed traffic treatments is inherently complicated.Although individual techniques in evaluating operations,safety,economic,and stakeholder objectives are available,a practical method that integrates all these risk factors and their uncertainties into a multiattribute decision-making tool is absent.A three-level project decision-making process was developed to model and assess multiple-attribute risk in a proposed traffic treatment from the perspective of multiple stakeholders.The direct benefits from reducing delay and safety risk(basic objectives of traffic treatments) are computed in Level 1 with established methods.Feasibility and performance analysis in Level 2 examine site-specific constraints and conduct detailed performance analysis using advanced analysis tools.In Level 3,this paper introduces an innovative and integrated multiple attributes evaluation process under fuzziness and uncertainty(MAFU) process for evaluation and decision-making.The MAFU is a comprehensive and systematic assessment and decision-making procedure that can assess the magnitudes of project performance and to integrate conflicting interests and tradeoffs among stakeholders.A case study illustrates theapplication of MAFU for the selection of a traffic alternative involving several evaluation attributes and stakeholders.Results show that the MAFU produced the smallest variance for each alternative.With traditional cost–benefit evaluation methods,the uncertainty associated with performance of a traffic project in terms of operation,safety,environmental impacts,etc.,is unrestricted and cumulative.Therefore,a reliable multi-attribute evaluation of complex traffic projects should not be made with conventional cost–benefit analysis alone but with a process like MAFU.

Deep-reinforcement-learning-based water diversion strategy

Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water and lake conditions.However,the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water.Here,we propose a new approach called dynamic water diversion optimization(DWDO),which combines a comprehensive water quality model with a deep reinforcement learning algorithm.We applied DWDO to a region of Lake Dianchi,the largest eutrophic freshwater lake in China and validated it.Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7%and 6%,respectively,compared to previous operations.Additionally,annual water diversion decreased by an impressive 75%.Through interpretable machine learning,we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion.We found that a single input variable could either increase or decrease water diversion,depending on its specific value,while multiple factors collectively influenced real-time adjustment of water diversion.Moreover,using well-designed hyperparameters,DWDO proved robust under different uncertainties in model parameters.The training time of the model is theoretically shorter than traditional simulation-optimization algorithms,highlighting its potential to support more effective decisionmaking in water quality management.

Flexible Active Distribution System Management Considering Interaction with Transmission Networks Using Information-gap Decision Theory

In this paper,a flexible management method is proposed for an active distribution system(ADS)with distributed energy resources(DERs)integrated,where DERs can provide spinning reserves to transmission networks.This method,based on the information-gap decision-making theory(IGDT)theory,could be of use to the ADS operator(ADSO)from either the opportunistic or robust perspective when reserve is called by the independent system operator(ISO).Two IGDT uncertainty models are employed to depict the characteristics of reserve uncertainty in centralized and decentralized control frameworks.The reactive power of each DER is managed by the ADSO in the immunity functions,which are reformulated as bi-level biobjective optimization problems.A hybrid multi-objective differential evolutional algorithm(MODE)is proposed to solve the optimization problems.The relationship between the uncertainty levels and robust/opportunistic limits is revealed by the Pareto fronts obtained by MODE.Effectiveness of the proposed method is demonstrated based on simulation results of a 33-bus and 123-bus test system.

Precautionary Effort Investment under Cross Risk Aversion

This paper deals with the effort problem under multiple risks in bivariate utility setting. We identify preference conditions to insure positive or negative effect of a background variable uncertainty on effort in the presence of other risks. We allow for the simultaneous presence of wealth and background variable uncertainties. We investigate the joint effect of two-source uncertainties on effort when two risks are either small or positive quadrant dependent. Our work extends the previous model of effort to bivariate utility framework and presents new insights into the issue of optimal effort under uncertainty.