Hierarchical Game Approach for Optimization of Regional Integrated Energy System Clusters Considering Bounded Rationality

Regional integrated energy system(RIES)cluster,i.e.,multi-source integration and multi-region coordination,is an effective approach for increasing energy utilization efficiency.The hierarchical architecture and limited information sharing of RIES cluster make it difficult for traditional game theory to accurately describe their game behavior.Thus,a hierarchical game approach considering bounded rationality is proposed in this paper to balance the interests of optimizing RIES cluster under privacy protection.A Stackelberg game with the cluster operator(CO)as the leader and multiple RIES as followers is developed to simultaneously optimize leader benefit and RIES utilization efficiency.Concurrently,a slight altruistic function is introduced to simulate the game behavior of each RIES agent on whether to cooperate or not.By introducing an evolutionary game based on bounded rationality in the lower layer,the flaw of the assumption that participants are completely rational can be avoided.Specially,for autonomous optimal dispatching,each RIES is treated as a prosumer,fexibly switching its market participation role to achieve cluster coordination optimization.Case studies on a RIES cluster verify effectiveness of the proposed approach.

Computational Intelligence Determines Effective Rationality

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.

Satisficing as an Account of Kuhnian Rationality

The lack of an account of rationality in The Structure of Scientific Revolutions was a lacuna which Thomas Kuhn acutely felt. In this paper, I argue that Herbert Simon＇s notion of ＂satisficing＂ provides a formally well-developed and empirically well-established theory of rationality that fits well with Kuhn＇s general characterization of science. I start by considering two rival interpretations of the problem of Kuhnian rationality and introduce Simon＇s notion of satisficing. In Section 3, I show how satisficing can be used to interpret paradigm, change, rational theory-choice, relativism, and progress. On this account, Kuhnian scientists are not irrational. Rather they employ the same computational mechanism which allows humans to play chess.

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Modeling commuters' route choice behavior under pre-trip information system

Research about the auto commuter＇s pre-trip route choice behavior ignores the combined influence of the real-time information and all respondents＇ historical information in the existing documents.To overcome this shortcoming,an approach to describing the pre-trip route choice behavior with the incorporation of the real-time and historical information is proposed.Two types of real-time information are investigated,which are quantitative information and prescriptive information.By using the bounded rationality theory,the influence of historical information on the real-time information reference process is examined first.Estimation results show that the historical information has a significant influence on the quantitative information reference process,but not on the prescriptive information reference process.Then the route choice behavior is modeled.A comparison is also made among three route choice models,one of which does not incorporate the real-time information reference process,while the others do.Estimation results show that the route choice behavior is better described with the consideration of the reference process of both quantitative and prescriptive information.

A topology-based bounded rationality day-to-day traffic assignment model

This paper analyzes the day-to-day adjustment process of users’behaviors in a transport network which is affected by relevant alterations such as disruptions due to critical events which cause the impossibility to use one or more links.For representing the progressive adjustment of the flows on the network to reach a new equilibrium,a dayto-day discrete-time model is proposed,based on the idea that people are bounded rational in their choices,i.e.they often do not behave according to the optimal solution but they accept solutions they consider satisfying.Users,in their choice process,are influenced by the topological similarity between the route they are currently using and others.This means that they tend to prefer the solutions that are more similar to the one they are already using.In parallel,users exhibit a myopic behavior,i.e.,they tend to overestimate the goodness of a route if,when using it,they suddenly experience a significant reduction in travel time compared to what they are used to.In the paper it is shown that such route choice behaviour implies that the steady state of the system corresponds to a Bounded Rational User Equilibrium,i.e.,a state that does not diverge from the user equilibrium more than a certain value which increases when the relative importance given to the topological similarity grows.The model also assumes that these biases vanish,at least with respect to those routes that are most frequently used by users,after a sufficient amount of time.Under certain conditions,it is then shown that the steady state can eventually collapse into a User Equilibrium.The effectiveness of the proposed model is assessed via simulation results in which two test networks are analyzed in detail to show the evolution of the users’behaviour in a transport network after a disruption.

Formulation and solution for calibrating boundedly rational activity-travel assignment:An exploratory study

Parameter calibration of the traffic assignment models is vital to travel demand analysis and management.As an extension of the conventional traffic assignment,boundedly rational activity-travel assignment(BR-ATA)combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns.The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models.In virtue of the multi-state supernetwork,this exploratory study formulates the BRATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem.Considering the temporal dimension,we also propose a dynamic formulation of the BR-ATA calibration problem.The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems.Numerical examples are presented to calibrate the activity-based travel demand for illustrations.The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.

Network traffic flow evolution with battery electric vehicles and conventional gasoline vehicles

In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.

Theories behind a nursing intern’s error in terms of clinical decision-making

Clinical decision-making and judgment is an essential skill that should be acquired by nursing interns.However,many studies reveal that a large proportion of nursing interns have difficulty in meeting the basic expectation of clinical decision-making.This article begins with a brief review of some theories related to clinical decision-making.Then it takes a case,for example,to show how the wrong decision is made.Furthermore,it makes a general analysis on the case drawing on the concept of bounded rationality,dual-process model,and skills acquisition theory.This article ends with some reflections from the case,to improve the consciousness and skills in decision-making for nursing interns.

Mechanism Design and Simulation Study of Environmental Pollution Improvement

In recent years, the researches on the theory and application of public articles and mechanism design at home and abroad has attracted more and more attentions. The paper uses the idea of mechanism design and simulation to analyze and solve environmental pollution improvement. By establishing a simple environmental pollution improvement model, the paper transforms pollution control into binary decision established by a sewage treatment plant and different decisions by the fisherman on the establishment of sewage treatment plants. The paper observes the results of applying different mechanisms and proposes the suggestions of implementin~ various pollution control methods.

The Generic Uniqueness and Well-Posedness of NashEquilibria for Stable Population Games

This paper aims at studying a new kind of stable population games introduced by J.Hofbauer and H.Sandholm in 2009.We first construct a complete distance space M consisting of stable population games and show that most of stable population games have unique Nash equilibrium point that according to Baire’s category theorem.It implies that every stable population game that possesses more than one Nash equilibrium can be approached arbitrarily by a sequence of the stable population game each of which has a unique Nash equilibrium.Then,we construct a bounded rationality function and deduce some results on the generic well-posedness implying Tikhonov well-posedness and Hadamard well-posedness for stable population games.

INVENTORY REPLENISHMENT POLICY FOR THE DECISION MAKER WITH PRESENT-BIASED PREFERENCE AND TIME-INCONSISTENCY UNDER INFLATION

Due to bounded rationality, people often rush for instantaneous gratification if the decisions involve immediate rewards, but procrastinate if the decisions involve immediate costs. Present-biased preference and time-inconsistency will be more salient when people are making inter-temporal decisions to trade off rewards and costs in the future. People outweigh the present and their preference for the outcome in the furore will decline over time. Incorporating present-biased preference and time-inconsistency under quasi-hyperbolic discounting, time value of money under inflation and time-varying demand with shortages completely backlogged, we develop an inventory replenishment model for a deteriorating item. Inventory replenishment policies that benefit the decision maker are as follows. With the increase of time-inconsistency, one can increase ordering number and decrease fraction of shortages; with the increase of hazard rate within an upper limit, one can increase ordering number and decrease fraction of shortage; with the increase of compounded discount rate, one can increase ordering number and fraction of shortage. Hyperbolic discounting increases the ordering number and decreases the final profit, and the degree of hyperbolic discounting strengthens such outcomes. As a result, the optimal policy for a decision maker is to keep as rational as possible when making inter-temporal decisions.

COMPLEX CONCEPT LATTICES FOR SIMULATING HUMAN PREDICTION IN SPORT

In order to address the study of complex systems,the detection of patterns in their dynamics could play a key role in understanding their evolution.In particular,global patterns are required to detect emergent concepts and trends,some of them of a qualitative nature.Formal concept analysis(FCA) is a theory whose goal is to discover and extract knowledge from qualitative data(organized in concept lattices).In complex environments,such as sport competitions,the large amount of information currently available turns concept lattices into complex networks.The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information.The complexity of concept lattices-considered as networks with complex topological structure- is analyzed.It is applied to building a knowledge based system for confidence-based reasoning,which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.

Well-Posedness and Structural Stability on a System of Simultaneous Generalized Vector Quasi-Equilibrium Problems

In this paper,we establish the stable results for a system of simultaneous general-ized vector quasi-equilibrium problems(SSGVQEP)by using its bounded rationality model.Under the abstract frame,a unified well-posedness on Hadamard types and Tikhonov types well-posedness for SSGVQEP is introduced.Moreover,sufficient condition for the well-posedness of SSGVQEP is given.Finally,we prove that the majority(in Baire category sense)of SSGVQEP is structural stability.

Simulation of consumers decision-making process using agent-based model approach

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network.The proposed model use theories and tools from the psychology of consumer behavior,social networks and complex dynamical systems like the Consumat framework and fuzzy logic.The model has been adjusted using real data,tested with the automobile market and it can recreate trends like those described in the world market.