arXiv subjects

Dragisa Zunic

Publications and source records attributed to Dragisa Zunic.

3 recordsLinked to original sources

arXiv2026

Emergent Behaviour in Financial Markets

Some properties of so-called complex or collective systems can be observed to emerge from the interactions of elementary agents. This phenomenon, known as emergent behaviour, has long since been studied in the most diverse disciplines, with recent growing awareness from the formal methods community about the opportunity of opening up to seemingly distant disciplines with appropriate technology for computer-aided reasoning. Different peculiar elements of complexity make automated reasoning on these systems particularly challenging. We consider electronic financial markets to drive our discussion. We identify and structure the sources of complexity to tackle in order to provide computational support for the analysis of emergent phenomena. We refrain from evaluating the suitability of specific technical solutions or frameworks of preference, which would as usual require simplifying assumptions and divert from the actual phenomenon of interest. Rather, we elaborate on possible alternatives to handle some of the main technical aspects involved in automated analysis, while retaining a solid and concrete interpretation of the domain, and in doing so outline a more systematic research program for the formal specification and analysis of market mechanisms.

cs.MA↗

arXiv2026

Fundamental market design as a layer of AI-agent alignment

This paper argues that AI-agent alignment in markets should not be understood only as a property of agents, but also as a property of the interaction infrastructure in which agents act. In financial markets, this infrastructure is the market core: the rule system that determines how orders enter, interact, match, persist, and stabilize. If this fundamental interaction layer allows or rewards undesired behaviour, then higher-level alignment of agents may be insufficient. We propose to view fundamental market design as a layer of AI-agent alignment. Alongside the important work of computational economics in modelling agents, strategies, and learning, we focus on a complementary but more fundamental layer: the formal modelling of the market core itself. Market design, especially at the level of the core mechanism, can benefit from a rigour characteristic of theoretical computer science. This gives a transparent-box model of the market, whose core properties can be formally specified and reasoned about. It also lets us treat the trading venue not as a static order book, but as a computational process combining resident orders with incoming order flow, and ask which computational model, perhaps yet unknown, naturally lies at its core. This perspective is especially relevant for markets populated by adaptive or AI agents. Such agents may learn what the mechanism rewards, including speed, delay, liquidity provision, or manipulation. These behaviours are not only properties of individual agents, but may emerge from the agent-mechanism system. We therefore argue that transparent formal models of market cores can support incentive-oriented analysis and the design of mechanisms in which desirable behaviours are structurally favoured and undesirable behaviours are harder to sustain.

cs.GT↗

arXiv2012

Computational interpretation of classical logic with explicit structural rules

We present a calculus providing a Curry-Howard correspondence to classical logic represented in the sequent calculus with explicit structural rules, namely weakening and contraction. These structural rules introduce explicit erasure and duplication of terms, respectively. We present a type system for which we prove the type-preservation under reduction. A mutual relation with classical calculus featuring implicit structural rules has been studied in detail. From this analysis we derive strong normalisation property.

cs.LO↗