Global food systems are at the center of some of the most pressing modern societal challenges: They are significant contributors to a range of systemic issues, including health problems and chronic diseases, greenhouse gas emissions and general environmental degradation, and increasing financial burdens on healthcare and economies. Within these complex systems, final sustainable consumption, which refers to the adoption of diets that are both healthy and environmentally friendly, plays a critical role. Significant changes in contemporary dietary patterns are essential to address the rising burden of chronic diseases and public health outcomes and the escalating climate crisis. Achieving these shifts requires coordinated action from policymakers, consumers, and the scientific community in an effort to support the development, implementation, and evaluation of advertising and policy instruments that promote healthier and more sustainable dietary choices. However, driving changes in dietary behavior is a complex challenge, shaped by the interplay of heterogeneous influences, including biological, social, cultural, environmental, political, and economic factors, and further complicated by the difficulty of validating proposed approaches in ways that are both efficient and ethically sound. This vision paper presents the problem of promoting healthy and environmentally friendly diets and their implications for environmental sustainability. In particular, it discusses a systems approach based on social network dynamics and social interventions, illustrating recent findings that demonstrate the potential of influence strategies to drive dietary change. Finally, key scientific challenges and emerging research opportunities are highlighted.

This paper addresses the synchronization problemin open multi-agent systems containing both cooperative andantagonistic interactions. In these systems, new agents can join and new interactions can be formed over time. Moreover, the types of interactions, cooperative or antagonistic, may change. To model these structural changes, we represent the system as a switched system interconnected over a dynamic signed graph. Using the signed edge-based agreement protocol and constructing strict Lyapunov functions for signed edge-Laplacian matrices with multiple zero eigenvalues, we establish global asymptotic stability of the synchronization errors. Numerical simulations validate our theoretical results.

The random utility model (RUM) is a fundamental notion in studies of human decision-making. However, RUM relies on the calibration of its choice function's weight parameter, usually interpreted as a rationality parameter, resulting in a case-dependence that undermines both interpretability and predictability of choices across experimental settings. We addressed this limitation by normalizing utilities in RUM and deriving a new choice parameter β, independent of case-specific prospects. Drawing from a novel interpretation of β in terms of the lowest perceived probability of unlikely events, we conducted an experimental survey in Swedish universities to infer β distributions, capturing the variability of probability perception among decision-makers. We tested these statistical models for β on two independent datasets exploring the framing effect. The results showed that the predictions align with the observed experimental data (Pearson's correlation greater than 94%), thereby indicating that the novel characterization of the choice parameter strengthens the predictive capabilities of RUM.

Diversity indices of quadratic type, such as fractionalization and Simpson index, are measures of heterogeneity in a population. Even though they are univariate, they have an intrinsic bivariate interpretation as encounters among the elements of the population. In the paper, it is shown that this leads naturally to associate populations to weakly balanced signed networks. In particular, the frustration of such signed networks is shown to be related to fractionalization by a closed-form expression. This expression allows to simplify drastically the calculation of frustration for weakly balanced signed graphs.

Substantial shifts in contemporary diets are needed to address the growing burden of chronic diseases and the accelerating climate crisis. To facilitate these changes, policy-makers must develop effective strategies to advertise and encourage healthy dietary choices among the population. In this work, to capture changes in dietary behavior we propose an opinion dynamics model where prejudiced agents can interact with their neighbors in a social network. Additionally, an external entity, such as a policy-maker or a government, can influence the agents through sequential campaigns towards a desired collective opinion. We investigate the impact of this exogenous influence under budget constraints, limiting, for example, the number of agents that can be influenced per campaign, and show how to optimize influence allocation. We conclude with an application of the proposed approach within the context of sustainability goals on food consumption set by the UK Climate Change Committee. Using baseline food intake patterns derived from survey data on the UK population, we evaluate the impact of campaigns promoting dietary shifts and mitigating environmental impacts.

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations.

Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called 'repelling' signed Laplacians, the marginal stability property (needed to achieve consensus) is not guaranteed a priori and, even when it holds, it does not automatically lead to consensus, as these signed Laplacians may lose rank even in strongly connected digraphs. Furthermore, in the time-varying case, instability can occur even when switching in a family of systems each of which corresponds to a marginally stable signed Laplacian with the correct corank. In this article, we present novel conditions for achieving consensus on signed digraphs based on the property of eventual positivity, a Perron-Frobenius (PF) type of property for signed matrices. The conditions we develop cover both time-invariant and time-varying cases. A particularly simple sufficient condition, valid in both cases, is that the Laplacians are normal matrices. Such condition can be relaxed in several ways. For instance, in the time-invariant case it is enough that the Laplacian has this PF property on the right side, but not on the left side (i.e., on the transpose). For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians. All conditions can be easily extended to bipartite consensus.

Even for nonnegative graphs, the pseudoinverse of a Laplacian matrix is not an “ordinary” (i.e., unsigned) Laplacian matrix but rather a signed Laplacian. In this paper, we show that the property of eventual positivity provides a natural embedding class for both signed and unsigned Laplacians, class which is closed with respect to pseudoinversion as well as to stability. Such a class can deal with both undirected and directed graphs. In particular, for digraphs, when dealing with pseudoinverse-related quantities such as effective resistance, two possible solutions naturally emerge, differing in the order in which the operations of pseudoinversion and of symmetrization are performed. Both lead to an effective resistance which is a Euclidean metric on the graph.

Every day we face numerous lifestyle decisions, some dictated by habits and some more conscious, which may or may not promote sustainable living. Aided by digital technology, sustainable behaviors can diffuse within social groups and inclusive communities. This paper outlines a longitudinal experimental study of social influence in behavioral changes toward sustainability, in the context of smart residential homes. Participants are residing in the housing on campus referred to as KTH Live-In Lab, whose behaviors are observed w.r.t. key lifestyle choices, such as food, resources, mobility, consumption, and environmental citizenship. The focus is on the preparatory phase of the case study and the challenges and limitations encountered during its setup. In particular, this work proposes a definition of sustainability indicators for environmentally significant behaviors, and hypothesizes that, through digitalization of a household into a social network of interacting tenants, sustainable living can be promoted. Preliminary results confirm the feasibility of the proposed experimental methodology.

 Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is thus crucial to improve energy efficiency without disregarding occupants' comfort. This paper investigates the influence of physical environmental variables (i.e., indoor and outside climate parameters) and categorical variables (i.e., time of the day) on occupants' behavior patterns related to window operation, utilizing a multivariate logistic regression analysis. The data considered in this study are collected during winter months, when the effect on the energy consumption of the window operation is the highest, at a Swedish residential building, the KTH Live-In Lab, accommodating four occupants in separate studio apartments. Although all the occupants seem to share a sensitivity to some common factors, such as air quality and time of the day, we can also observe individual variability with respect to the most significant drivers influencing window operation behaviors.