Search

The optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375.

AbstractThis article proposes and studies a new type of constant elasticity of volatility (CEV) model, titled LVO-CEV, where the name indicates that the excess return is linear in the volatility. The model has either strong or weak solutions, depending on the elasticity parameter, thanks to a connection to radial Ornstein-Uhlenbeck processes. Attainability of lower bounds and the existence of pricing measures are provided. The model allows for closed-form solutions in the context of expected utility theory for investors with hyperbolic absolute risk aversion (HARA) utilities on terminal wealth and consumption. We estimate and implement our model as well as other popular models of indexes and stocks, providing a fair comparison in portfolio management. [ABSTRACT FROM AUTHOR]

We propose an alternative dynamic theory of coalition breakdown. Motivated by recent coalition-splitting events of unilateral country withdrawals, we assume that: (i) the payoff-sharing rule within coalitions is not necessarily set according to any optimality and/or stability criterion and (ii) players initially behave as if the coalition will last forever. If the sharing rule is non-negotiable or if renegotiation is very costly, compliance with these rules may become unbearable for a given member because the rule, being too rigid, would make exit preferable as time passes. We examine this endogenous exit problem in the case of time-invariant sharing rules. Assuming a Nash non-cooperative game after a (potential) split where players play Markovian strategies, we characterize the solutions of the endogenous exit problem in a linear-quadratic frame with endogenous splitting time. We find that splitting countries are precisely those that used to benefit the most from the coalition. Suitable sharing rules should be used to prevent coalition splitting. When initial pollution is high, all shares should be low enough and none of the players should receive a payoff share larger than 1/2. If initial pollution is low, we provide an explicit interval for sharing-rule values to prevent the collapse of the coalition. Finally, we demonstrate that the latter properties are qualitatively consistent with the optimal behaviors and equilibrium outcomes resulting from players anticipating the end of the coalition and acting accordingly. [ABSTRACT FROM AUTHOR]

The variety of available technology options for the operation of zero-emission bus systems gives rise to the problem of finding an optimal technology decision for bus operators. Among others, overnight charging, opportunity charging and hydrogen-based technology options are frequently pursued technological solutions. As their operating conditions are strongly influenced by the urban context, an optimal technology decision is far from trivial. In this paper, we propose an Integer Linear Programming (ILP) based optimization model that is built upon a broad input database, which allows a customized adaption to local circumstances. The ultimate goal is to determine an optimal technology decision for each bus line, considering its combined effects on charging and vehicle scheduling as well as infrastructural design. To this end, we develop technology-specific network representations for five distinct technologies. These networks can be viewed individually or as a multi-layered graph, which represents the input for the optimal technology mix. The proposed optimization framework is applied to a real-world instance with more than 4.000 timetabled trips. To study the sensitivity of solutions, parameter changes are tested in a comprehensive scenario design. The subsequent analysis produces valuable managerial insights for the bus operator and highlights the decisive role of certain planning assumptions. The results of our computations reveal that the deployment of a mixed fleet can indeed lead to financial benefits. The comparison of single technology system solutions provides a further basis for decision making and demonstrates relative superiorities between different technologies. [ABSTRACT FROM AUTHOR]

This paper considers the non-zero-sum stochastic differential game problem between two ambiguity-averse insurers (AAIs) with common shock. Each AAI's surplus process consists of a proportional reinsurance protection and an investment in a money account, a stock and a credit default swap (CDS) with the objective of maximizing the expected utility of her relative terminal surplus with respect to that of her competitors. We consider default contagion risk of CDSs through a Markovian model with interacting default intensities. It is worthwhile to consider the uncertainty of the model on both the insurer herself and her competitors. In our model, we describe the surplus processes of two insurers by two jump-diffusion models with a common shock. Under jump-diffusion models, the robust Nash equilibrium strategies and the value functions for the all-default, one-default and all-alive case are derived under a worst-case scenario, respectively. Finally, through some numerical examples, we found some interesting results about the effects of some model parameters on the robust Nash equilibrium strategies, such as, the common shocks and the individual claims have the opposite effect on reinsurance investment. [ABSTRACT FROM AUTHOR]

We develop an AK model augmented in two directions: first, by modeling nature as a productive externality, and, second, by considering a process of natural capital accumulation where a reproduction force is moderated by the negative impact of productive pollution. Differently from the existing literature, more focused on the planner's solution, we study here a market economy and the effects of its imperfections. We introduce two alternative natural accumulation processes: a power law, more usual in economics, and a logistic law, more popular in ecology. In both cases, we observe the occurrence of cycles of period two and of period a power of two, and chaos through a flip bifurcation and period-doubling bifurcations. In the case of the power law, dynamics are even richer because of the occurrence of limit cycles through a Neimark-Sacker bifurcation. Worth to note, in this case, cycles occur under a larger Total Factor Productivity, which amplifies the negative impact of pollution on natural regeneration. In the case of a logistic law, cycles take place when the saturation effect of nature has a larger negative impact on the reproduction process. [ABSTRACT FROM AUTHOR]