Exploiting simple corporate memory in iterative coalition games

Amongst the challenging problems that must be addressed in order to create increasingly automated electronic commerce systems are those which involve forming coalitions of agents to exploit a particular market opportunity. Furthermore economic systems are normally continuous dynamic systems generating many instances of the same or similar problems (the regular calls for tender, regular emergence of new markets etc.).The work described in this paper explores how simple forms of memory can be exploited by agents over time to guide decision making in iterative sequences of coalition formation problems enabling them to build up social knowledge in order to improve their own utility and the ability of the population to produce increasingly well suited coalitions for a simple call-for-tender economy.Postprint (published version

Controlling herding in minority game systems

abstract: Resource allocation takes place in various types of real-world complex systems such as urban traffic, social services institutions, economical and ecosystems. Mathematically, the dynamical process of resource allocation can be modeled as minority games. Spontaneous evolution of the resource allocation dynamics, however, often leads to a harmful herding behavior accompanied by strong fluctuations in which a large majority of agents crowd temporarily for a few resources, leaving many others unused. Developing effective control methods to suppress and eliminate herding is an important but open problem. Here we develop a pinning control method, that the fluctuations of the system consist of intrinsic and systematic components allows us to design a control scheme with separated control variables. A striking finding is the universal existence of an optimal pinning fraction to minimize the variance of the system, regardless of the pinning patterns and the network topology. We carry out a generally applicable theory to explain the emergence of optimal pinning and to predict the dependence of the optimal pinning fraction on the network topology. Our work represents a general framework to deal with the broader problem of controlling collective dynamics in complex systems with potential applications in social, economical and political systems.The final version of this article, as published in Scientific Reports, can be viewed online at: https://www.nature.com/articles/srep2092

The Delineation and Apportionment of an EU Consolidated Tax Base for Multi-jurisdictional Corporate Income Taxation: a Review of Issues and Options.

The Commission Services study on Company Taxation in the Internal Market and the Communications COM(2001)582 and COM(2003)726 on EU company taxation presented a long-term strategy to tackle the corporate income tax obstacles in the Internal Market by providing multi-jurisdictional companies with a consolidated tax base for their EU-wide activities for corporate taxation purposes. This comprehensive approach relies on a number of crucial steps such as delineating the CTB and choosing the mechanism for apportioning the multinationals' tax bases between the relevant Member States, so that they can then apply the national corporation tax rate to their respective shares. This work systematically addresses some of the fundamental questions that arise when considering the design of a consolidation + apportionment system for sharing multinationals' consolidated profits between EU Member States.European Union, Corporate Taxation, multinational taxation, formula apportionment

Human Path Prediction using Auto Encoder LSTMs and Single Temporal Encoders

Due to automation, the world is changing at a rapid pace. Autonomous agents have become more common over the last several years and, as a result, have created a need for improved software to back them up. The most important aspect of this greater software is path prediction, as robots need to be able to decide where to move in the future. In order to accomplish this, a robot must know how to avoid humans, putting frame prediction at the core of many modern day solutions. A popular way to solve this complex problem of frame prediction is Auto Encoder LSTMs. Though there are many implementations of this, at its core, it is a neural network comprised of a series of time sensitive processing blocks that shrink and then grow the data’s dimensions to make a prediction. The idea of using Auto Encoder styled networks to do frame prediction has also been adapted by others to make Temporal Encoders. These neural networks work much like traditional Auto Encoders, in which the data is reduced then expanded back up. These networks attempt to tease out a series of frames, including a predictive frame of the future. The problem with many of these networks is that they take an immense amount of computation power, and time to get them performing at an acceptable level. This thesis presents possible ways of pre-processing input frames to these networks in order to gain performance, in the best case seeing a 360x improvement in accuracy compared to the original models. This thesis also extends the work done with Temporal Encoders to create more precise prediction models, which showed consistent improvements of at least 50% for some metrics. All of the generated models were compared using a simulated data set collected from recordings of ground level viewpoints from Cities: Skylines. These predicted frames were then analyzed using a common perceptual distance metric, that is, Minkowski distance, as well as a custom metric that tracked distinct areas in frames. All of the following was run on a constrained system in order to see the effects of the changes as they pertain to systems with limited hardware access

Mechanism Design and Communication Networks

This paper characterizes the class of communication networks for which, in any environment (utilities and beliefs), every incentive-compatible social choice function is (partially) implementable. Among others, in environments with either common and independent beliefs and private values or a bad outcome, we show that if the communication network is 2-connected, then any incentive-compatible social choice function is implementable. A network is 2-connected if each player is either directly connected to the designer or indirectly connected to the designer through at least two disjoint paths. We couple encryption techniques together with appropriate incentives to secure the transmission of each player’s private information to the designer.Mechanism design; incentives; Bayesian equilibrium; communication networks; encryption; secure transmission; coding

Strategic Basins of Attraction, the Farsighted Core, and Network Formation Games

We make four main contributions to the theory of network formation. (1) The problem of network formation with farsighted agents can be formulated as an abstract network formation game. (2) In any farsighted network formation game the feasible set of networks contains a unique, finite, disjoint collection of nonempty subsets having the property that each subset forms a strategic basin of attraction. These basins of attraction contain all the networks that are likely to emerge and persist if individuals behave farsightedly in playing the network formation game. (3) A von Neumann Morgenstern stable set of the farsighted network formation game is constructed by selecting one network from each basin of attraction. We refer to any such von Neumann-Morgenstern stable set as a farsighted basis. (4) The core of the farsighted network formation game is constructed by selecting one network from each basin of attraction containing a single network. We call this notion of the core, the farsighted core. We conclude that the farsighted core is nonempty if and only if there exists at least one farsighted basin of attraction containing a single network. To relate our three equilibrium and stability notions (basins of attraction, farsighted basis, and farsighted core) to recent work by Jackson and Wolinsky (1996), we define a notion of pairwise stability similar to the Jackson-Wolinsky notion and we show that the farsighted core is contained in the set of pairwise stable networks. Finally, we introduce, via an example, competitive contracting networks and highlight how the analysis of these networks requires the new features of our network formation model.Basins of attraction, Network formation, Supernetworks, Farsighted core, Nash networks

Enabling cooperative and negotiated energy exchange in remote communities

Energy poverty at the household level is defined as the lack of access to electricity and reliance on the traditional use of biomass for cooking, and is a serious hindrance to economic and social development. It is estimated that 1.3 billion people live without access to electricity and almost 2.7 billion people rely on biomass for cooking, a majority of whom live in small communities scattered over vast areas of land (mostly in the Sub-Saharan Africa and the developing Asia). Access to electricity is a serious issue as a number of socio-economic factors, from health to education, rely heavily on electricity. Recent initiatives have sought to provide these remote communities with off-grid renewable microgeneration infrastructure such as solar panels, and electric batteries. At present, these resources (i.e., microgeneration and storage) are operated in isolation for individual home needs, which results in an inefficient and costly use of resources, especially in the case of electric batteries which are expensive and have a limited number of charging cycles. We envision that by connecting homes together in a remote community and enabling energy exchange between them, this microgeneration infrastructure can be used more efficiently. Against this background, in this thesis we investigate the methods and processes through which homes in a remote community can exchange energy. We note that remote communities lack general infrastructure such as power supply systems (e.g., the electricity grid) or communication networks (e.g., the internet), that is taken for granted in urban areas. Taking these challenges into account and using insights from knowledge domains such game theory and multi-agent systems, we present two solutions: (i) a cooperative energy exchange solution and (ii) a negotiated energy exchange solution, in order to enable energy exchange in remote communities.Our cooperative energy exchange solution enables connected homes in a remote community to form a coalition and exchange energy. We show that such coalition a results in two surpluses: (i) reduction in the overall battery usage and (ii) reduction in the energy storage losses. Each agents's contribution to the coalition is calculated by its Shapley value or, by its approximated Shapley value in case of large communities. Using real world data, we empirically evaluate our solution to show that energy exchange: (i) can reduce the need for battery charging (by close to 65%) in a community; compared with when they do not exchange energy, and (ii) can improve the efficient use of energy (by up to 10% under certain conditions) compared with no energy exchange. Our negotiated energy exchange solution enables agents to negotiate directly with each other and reach energy exchange agreements. Negotiation over energy exchange is an interdependent multi-issue type of negotiation that is regarded as very difficult and complex. We present a negotiation protocol, named Energy Exchange Protocol (EEP), which simplifies this negotiation by restricting the offers that agents can make to each other. These restrictions are engineered such that agents, negotiation under the EEP, have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes (within restricted the set of offers) in a decentralised manner. Using real world data, we empirically evaluate our protocol and show that, in this instance, a society of agents can: (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%, compared to when they do not exchange energy