Kurt Nielsen

Automated market makers (AMM) have grown to obtain significant market share within the cryptocurrency ecosystem, resulting in a proliferation of new products pursuing exotic strategies for horizontal differentiation. Yet, their theoretical properties are curiously homogeneous when a set of basic assumptions are met. In this paper, we start by presenting a universal approach to deriving a formula for liquidity provisioning for AMMs. Next, we show that the constant function market maker and token swap market maker models are theoretically equivalent when liquidity reserves are uniform. Proceeding with an examination of AMM market microstructure, we show how non-linear price effect translates into slippage for traders and impermanent losses for liquidity providers. We proceed by showing how impermanent losses are a function of both volatility and market depth and discuss the implications of these findings within the context of the literature.

In the present state of information technologies, Big Data and Smart Metering allow very precise readings of human behavior, like e.g. energy consumption from households and firms. Focusing on detailed micro-data profiles representing the users' preferences through time, preferences have to be represented for aggregation into an initial set of informative classes. For such an initial classification, a general frame is required which can be later exploited according to a specific purpose/application, while appropriately handling the uncertainty in human behavior classification. For the former, it is noticed that the aggregation of preferences profiles should maintain its characteristic shape, thus proposing a method for inferring class membership on the basis of proximity between any given profile, and specific (target) examples of the desired profiles. For the latter, a classification based on opposites and neutral categories is suggested. In consequence, identifying the profiles that should be targeted for specific applications, as in market design for demand response. The proposed methodology is applied to the energy sector, using a sample of 1243 firms in Denmark and their hourly energy use throughout 2013, classifying and benchmarking firms according to their green energy efficiency.

In this paper we explore multi-dimensional preference estimation from imprecise (interval) data. Focusing on different multi-criteria decision models, such as PROMETHEE, ELECTRE, TOPSIS or VIKOR, and their extensions dealing with imprecise data, preference modeling is examined with respect to a suggested set of axioms. Their performance is evaluated and some specific problems are identified, regarding the satisfaction of the proposed axioms as well as some difficulty on how to properly understand the uncertainty of the interval-valued outcome and its respective preference situation. In consequence, the Weighted Overlap Dominance (WOD) method is examined, which has been explicitly designed for interval data, thus satisfying the proposed axioms, and clearly identifying the preference relational situation for all pairs of alternatives.

Procurement systems are the basis for assuring efficiency and fairness in organizations. Consequently, the development of procurement systems faces an ongoing challenge in designing trading systems that facilitate transparent competition on both price and multiple attributes, as well as ensuring sufficient flexibility for operational purposes. As multi-attribute auctions may specify transparent rules for the procurement game, they allow for less flexibility as they rely on precise scores. Here we propose a decision support methodology, based on multi-criteria (out)ranking techniques, for enhancing the flexibility of the procurement process and its reliability for decision making under uncertainty.