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In this article, we present an extended data-fitting model which involves different and conflicting criteria, and we propose an algorithm based on a scalarization technique to solve it. Our model integrates in a unique framework three different criteria, namely, a data-fitting term, and the entropy and the sparsity of the set of unknown parameters. This model can be analyzed by means of multiple criteria decision-making techniques. We then validate the proposed modified algorithm using two computational experiments: We analyze the problem of handwritten digit recognition using a logistic regression model and a deep neural network model, respectively. In the final part of the article, we employ this methodology to forecasting instead. Given the importance of forecasting techniques to predict the future, which in turn can lead to positive impacts on firm performance, we propose two numerical experiments focusing on the forecast of the US GDP. In the first one, we proceed by means of a modified iterated function system with grayscale maps-type fractal operator, and, in the second one, we implement a modified neural network-based model.

Increasingly visible climate change consequences challenge carbon-based economies worldwide. While expert knowledge on climate change percolates through political initiatives and public awareness, its translation into large-scale policy actions appears limited. Climate change consequences unequally target regions, countries and social classes, a vital issue for social cooperation. When facing an imminent ecological collapse, in which conditions can self-interested agents gain environmental awareness and settle on a sustainable path of actions when their knowledge of the imminent collapse is bounded? This cooperation emerges from the interaction between individuals and the interaction of various cognitive processes within individuals. This article develops an agent-based model for this emergence of cooperation enriched with the Agent Zero neurocognitive grounded cognitive architecture. We investigate when agents endowed with deliberative, affective and social modules can settle on actions that safeguard their environment through numerical simulations. Our results show that cooperation on sustainable actions is the strongest when the system is at the edge of collapse. Policy measures that increase the environment’s resilience become internalized by the agents and undermine awareness of the ecological catastrophe. Depending on the cognitive channels activated, agent behaviors and reactions to specific interventions significantly vary. Our analysis suggests that taking different cognitive channels, deliberative, affective, social, and others into account, significantly impact results. The complexity of agent cognition deserves more attention to assess parameter sensitivity in social simulation models.

We develop an epidemiological approach to analyze how financial contagion may affect and be affected by economic activity. We show that, according to specific parameter values, the economy may converge either to a non-speculative or to a speculative equilibrium: in the former situation the level of per capita income is maximal, while in the latter it is reduced by financial contagion. The presence of economic and financial feedback effects may also give rise to macroeconomic fluctuations during the transitional path, clearly showing that such economic and financial links are an important driver of the short run macroeconomic performance. By extending the analysis to a spatial dimension, we also show that financial contagion in some specific region may propagate quickly also in regions far away from those in which the contagion initially occurs, highlighting the role of regional policy coordination to avoid interregional contagion.

Goal programming is a simple and widely used technique to solve policy-making problems involving different and conflicting objectives or criteria. It consists in minimizing for each criterion the distance between the achieved level and its respective goal. Weighted goal programming (WGP) is suitable to the analysis of macroeconomic policies involving several competing objectives with preferred weights of importance as it allows finding the optimal strategy for global sustainability which implies several coexisting goals. In this chapter, we propose a WGP model that can be used to determine the optimal allocation of labor in each economic sector in order to minimize the deviations from the goals of four different criteria which model economic, environmental, energetic, and social objectives. We apply our model to each country of the European Union and measure their performance with respect to the Europe 2020 agenda. Our model provides insights and policy recommendations, such as a better integration of the incoming workforce in a context of increasing immigration flows, development of renewable sources of energy, and green sustained transformation of national economic environments.

The complexity of reality can be better represented by models able to involve uncertainty and time patterns. We present a general formulation of a stochastic dynamic multiobjective optimization model and we provide different solution concepts based on its transformation into different deterministic equivalent models. We provide two applications to sustainable decision making in portfolio management and optimal workforce allocation.

We analyze the determination of the optimal intensity and duration of social distancing policy aiming to control the spread of an infectious disease in a simple macroeconomic–epidemiological model. In our setting the social planner wishes to minimize the social costs associated with the levels of disease prevalence and output lost due to social distancing, both during and at the end of epidemic management program. Indeed, by limiting individuals’ ability to freely move or interact with others (since requiring to wear face mask or to maintain physical distance from others, or even forcing some businesses to remain closed), social distancing has on the one hand the effect to reduce the disease incidence and on the other hand to reduce the economy’s productive capacity. We analyze both the early and the advanced epidemic stage intervention strategies highlighting their implications for short and long run health and macroeconomic outcomes. We show that both the intensity and the duration of the optimal social distancing policy may largely vary according to the epidemiological characteristics of specific diseases, and that the balancing of the health benefits and economic costs associated with social distancing may require to accept the disease to reach an endemic state. Focusing in particular on COVID-19 we present a calibration based on Italian data showing how the optimal social distancing policy may vary if implemented at national or at regional level.

We propose an agent-based model that explores the dynamics of a collective-social risk dilemma, within the framework of a simplified shallow lake model (Scheffer 1998). The agents are endowed with realistic cognitive abilities, building on the Agent Zero framework (Epstein, 2014). Deliberative, affective and social components are behind the agent decisions, accounting for both bounded rationality and the plurality of channels by which dispositions to act and opinions are transmitted. Individual decisions, aggregated over the entire population, may push a shallow lake over a regime shift threshold where a polluted (turbid) state settles in. This multifaceted model allows us to obtain a more precise understanding of the emergent process of cooperation induced by environmental awareness, and in so doing, it helps us understand how to possibly prevent the transition to the polluted state. To that purpose, we consider various cases on the combination of cognitive modules, and different multilayered network topologies in the population of agents. Simulation results, analyzed with novel computational methods, emphasize that environmental awareness is maximized when the socio-environmental system converges to self-organized criticality. Improved natural conditions, such as higher natural pollution decay, does not result in higher awareness as the agents internalize these constraints and are encouraged to pollute more. These results provide an insightful explanation behind the experimental result that more resistant environments (lower probability of collapse) may shelter a less environmentally concerned population, and that cooperation in the context of collective-social risk dilemma is the strongest when the system is at the edge of collapse.

We present an alternative to the geometric Brownian motion in order to model random shocks in economics, by focusing on the stochastic logistic process, which is a natural generalization of the geometric Brownian motion. We describe some potential applications in the context of economic growth, and show that its degree of tractability is very similar to that of the geometric Brownian motion, and thus its use can effectively improve the limits (related to the presence of a constant drift) of the geometric Brownian motion to model uncertainty.

We analyze the spatio-temporal dynamics of a simple model of macroeconomic geography in which demography and pollution dynamics mutually affect each other. Pollution, by reducing the carrying capacity of the natural environment – which determines the maximum amount of people a given location can effectively bear – crucially affects labor force dynamics which in turn alter the amount of resources available for abatement activities aiming to reduce pollution. Such mutual links determine the eventual sustainability of the development process in different locations and economies, and spatial interactions further complicate the picture. We show that neglecting the existence of mutual feedback between population and pollution leads to misleading conclusions about the eventual sustainability of a specific location. We also show that even neglecting the existence of spatial externalities can lead to misleading conclusions about the sustainability of different regions in the spatial economy. This suggests thus that both the nature of the population and pollution relationship and geographical factors may play a critical role in the process of sustainable development.

In the following paper it is proposed a model for controlling the Transfer Pricing risk of a Multinational Firm operating in different countries and that has a well-defined value chain spread across its different controlled companies. These types of agreements controlling such transactions generally take into account different type of objectives. Another problem arises by the process itself that may appear to be fairly deterministic; such simplistic assumption decays if the focus is placed on the general length of such agreements that tend to occupy a medium length planning horizon. Because of that, a Robust Multicriteria Transfer Pricing risk model is built, using the multiobjective capabilities of the Goal Programming approach and the uncertainty modeling features provided by the concept of Robust Optimization. The final result is a model in order to handle the possible worst-case scenarios in an environment of high uncertainty and mid to long-term planning.

Purpose Energy and environmental concerns have gained a significant role in public policy agenda as well as in energy economics literature. As policies often rely on imprecise information on data and goals, fuzzy goal programming (FGP) modeling is a relevant choice to evaluate multi-criteria sustainability. This technique is suitable for the analysis of the Europe 2020 strategy plan dealing with several possibly conflicting objectives in economy, environment, energy and employment. The paper aims to discuss these issues. Design/methodology/approach The paper presents a FGP model for sustainable implementations for all European Union (EU) countries with respect to Europe 2020 policy goals and provides insights for decision makers to better satisfy conflicting criteria by suggesting optimal allocations of workers in several economic sectors. Findings The analysis shows an overall great performance of European Union countries in the environmental and social criteria and outlines the needs for significant additional policy measures to reduce energy consumption while increasing the economic output. Comparing the performance of countries within the European Union between those who adopted the euro and those who maintained national currencies shows that Euro countries tend to perform worse in terms of Europe 2020 sustainability, opening opportunities for further research to better investigate on the causes and determinants of these differences. Originality/value The paper presents a conceptual model of sustainable development that improves understanding of the concept and reconciles highly competing policy objectives in a common framework. Applying this model to all European Union countries offers both comparison and policy recommendations at a large new scale.

In this paper, we propose an artificial market to model high-frequency trading where fast traders use threshold rules strategically to issue orders based on a signal reflecting the level of stochastic liquidity prevailing on the market. A market maker is in charge of adjusting prices (on a fast scale) and of setting closing prices and transaction costs on a daily basis, controlling for the volatility of returns and market activity. We first show that a baseline version of the model with no frictions is able to generate returns endowed with several stylized facts. This achievement suggests that the two time scales used in the model are one (possibly novel) way to obtain realistic market outcomes and that high-frequency trading can amplify liquidity shocks. We then explore whether transaction costs can be used to control excess volatility and improve market quality. While properly implemented taxation schemes may help in reducing volatility, care is needed to avoid excessively curbing activity in the market and intensifying the occurrence of abnormal peaks in returns.

We analyze the spatio-temporal dynamics of a simple model of economic geography in which population and pollution dynamics are mutually interdependent. Pollution by reducing the carrying capacity of the natural environment, which determines the maximum amount of people a given location can effectively bear, affects labor force dynamics which in turn alter pollution emissions. Such mutual links determine the development path followed by different locations, and spatial interactions further complicate the picture. We show that neglecting the existence of spatial externalities can lead to misleading predictions about the development path followed by different locations in the spatial economy.

This paper presents a purely dynamic economic-environmental growth model with a S-shaped production function and a pollution externality. We first outline the role of the saving rate in inducing/suppressing a dual steady-state, one with lower capital and pollution (the “green” steady state) than the other (the “dirty” steady state). Assuming the saving rate is such that the economy possesses a dual steady state, the second part of the paper exploits global analysis tecniques to study conditions such that the economy is able to escape the dirty steady state and approach the green steady state.

Most real-world optimization problems involve numerous conflicting criteria, imprecise information estimates and goals, thus the stochastic goal programming method offers an analytical framework to model and solve such problems. In this paper, we develop a stochastic goal programming model with satisfaction function that integrates optimal resource (labor) allocation to simultaneously satisfy conflicting criteria related to economic development, energy consumption, workforce allocation, and greenhouse gas emissions. We validate the model using sectorial data obtained from diverse sources on vital economic sectors for the United Arab Emirates. The results offer significant insights to decision makers for strategic planning decisions and investment allocations towards achieving long term sustainable development goals.

We analyze the implications of pollution and migration externalities on the optimal population dynamics in a spatial setting. We focus on a framework in which pollution affects the mortality rate and decreases utility, while migration occurs within the spatial economy. Agents optimally determine their fertility rate which, along with pollution-induced mortality and spatial migration, determines the net population growth rate. This setting implies that human population follows an endogenous logistic-type dynamics where fertility choices determine what the optimal limit of human population will be. We compare the decentralized and the centralized outcomes showing that such fertility decisions generally differ, quantifying the extent to which pollution and migration induced externalities matter in determining the difference between the two outcomes. We show that, due to the effects of pollution on utility and mortality, both the optimal fertility rate and the population size are smallest in the centralized economy but migration effects change not only the size of these differences but also their direction, suggesting that the spatial channel is an important mechanism to account for in the process of policymaking.

Goal Programming (GP) is a relevant and simple technique in Operations Research that helps Decision Makers in solving problems involving conflicting and competing criteria and objectives. A GP program is a distance-based model that involves the minimization of the difference between the achievement level of a certain criterion and its goal. GP models when combined with the notion of satisfaction or utility function provide practical advantage to the decision maker to include a system of preferences. In this paper, we present a GP model with satisfaction function that concurrently incorporates criteria on number of employees across various economic sectors and multiple sustainability goals on GDP growth, electricity consumption and Greenhouse Gas (GHG) emissions. The presented model offers critical inputs in planning and implementation of amenable strategies towards sustainable development. We validate the proposed model with data on vital economic sectors of the United Arab Emirates (UAE) towards achieving year 2030 goals.

Dynamic multiple criteria decision making (DMCDM) represents an extension of classical multiple criteria decision making to a con-text in which all variables are depending on time. This complex de-cision making problem requires the development of methodologies able to incorporate different and conflicting goals in a satisfying design of policies. We formulate two different goal programming models, namely a weighted goal programming model and a goal programing model with satisfaction functions, for solving DMCDM models. We present an application of this methodology to analyze the trade-off between consumption and investment in a traditional Ramsey-type (1928) macroeconomic model with heterogeneous agents. For a specific realistic parameterization, such a model is solved by means of the proposed goal programming formulations.

We analyze the spatio-temporal dynamics of capital and pollution in an economic growth model with purposive environmental protection activities. The production process of a unique homogeneous good generates pollution, thus the increases in output associated with economic growth tend to rise the stock of pollution. Pollution is a negative production externality which thus feeds back on the economy lowering the level of output; in order to compensate for such a negative effect associated with economic development, pollution is reduced by publicly funded abatement activities. We firstly consider a Solow-type framework in which economic and environmental policies are completely exogenous, and then we move to a Ramsey-type context in which they are endogenously determined. We analyze the spatio-temporal dynamics of the model economy through numerical simulations, and we consider two different specifications of the production function (a globally concave and a convex–concave technology) in order to stress the role that eventual poverty traps might play on both economic and environmental outcomes. We show that in the convex–concave technology framework, whenever rich regions are substantially rich diffusion can help poor regions to escape their poverty traps; if however they are not rich enough diffusion might condemn also rich regions to collapse. However, even if rich regions are particularly rich whenever the pollution externality is strong, the whole spatial economy might be doomed to collapse.