Michail Makridis

Kevin Riehl, Anastasios Kouvelas, Michail Makridis

City road infrastructure is a public good, and over-consumption by self-interested, rational individuals leads to traffic jams. Congestion pricing is effective in reducing demand to sustainable levels, but also controversial, as it introduces equity issues and systematically discriminates lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism, that employs an artificial currency different from money, that incentivizes cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable resource allocations by being aligned with consumers' needs rather than their financial power. This work highlights the value proposition of Karma, gives guidance on important Karma mechanism design elements, and equips the reader with a useful software framework to model Karma economies and predict consumers' behaviour. A case study demonstrates the potential of this feasible alternative to money, without the burden of additional fees.

Kevin Riehl, Michail Makridis, Anastasios Kouvelas

Advancements in computer science, artificial intelligence, and control systems of the recent have catalyzed the emergence of cybernetic societies, where algorithms play a significant role in decision-making processes affecting the daily life of humans in almost every aspect. Algorithmic decision-making expands into almost every industry, government processes critical infrastructure, and shapes the life-reality of people and the very fabric of social interactions and communication. Besides the great potentials to improve efficiency and reduce corruption, missspecified cybernetic systems harbor the threat to create societal inequities, systematic discrimination, and dystopic, totalitarian societies. Fairness is a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. Therefore, this work proposes a quantitative, transactional, distributive fairness framework, which enables systematic design of socially feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies.

Kevin Riehl, Anastasios Kouvelas, Michail Makridis

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource allocating contexts, money faces social, ethical, and legal constraints. Consequently, research increasingly explores artificial currencies and non-monetary markets, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks of conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road-map for future research directions.

Kevin Riehl, Anastasios Kouvelas, Michail Makridis

Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximizing system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broader social impacts, and the fact that users are heterogeneous in their urgency and experience different costs when being delayed. Existing strategies to account for the differences in needs of users in traffic management cover dedicated transit lanes, prioritization of emergency vehicles, transit signal prioritization, and economic instruments. Even though they are the major bottleneck for traffic in cities, no dedicated instrument that enables prioritization of individual drivers at intersections. The Priority Pass is a reservation-based, economic controller that expedites entitled vehicles at signalized intersections, without causing arbitrary delays for not-entitled vehicles and without affecting transportation efficiency de trop. The prioritization of vulnerable road users, emergency vehicles, commercial taxi and delivery drivers, or urgent individuals can enhance road safety, and achieve social, environmental, and economic goals. A case study in Manhattan demonstrates the feasibility of individual prioritization (up to 40\% delay decrease), and quantifies the potential of the Priority Pass to gain social welfare benefits for the people. A market for prioritization could generate up to 1 million \$ in daily revenues for Manhattan, and equitably allocate delay reductions to those in need, rather than those with a high income.

Alexander Genser, Noel Hautle, Michail Makridis et al.

Accurate estimation of the traffic state over a network is essential since it is the starting point for designing and implementing any traffic management strategy. Hence, traffic operators and users of a transportation network can make reliable decisions such as influence/change route or mode choice. However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more. To provide a better understanding of this problem, we organized an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras by the city's authorities, processed video data, and the Google Distance Matrix. We assess the different data sources, and we propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources. Comparative results with ground-truth data (derived from video measurements) show the efficiency and robustness of the proposed methodology.