Modeling endogenous learning and imperfect competition effects in climate change economics

In this two-part paper we evaluate the effect of "endogenizing” technological learning and strategic behavior of agents in economic models used to assess climate change policies. In the first part we show the potential impact of R&D policies or demonstration and deployment (D&D) programs in the context of stringent stabilization scenarios. In the second part we show how game-theoretic methods can be implemented in climate change economic models to take into account three types of strategic interactions: (i) the market power of the countries benefiting from very low abatement costs on international markets for CO2 emissions, (ii) the strategic behavior of governments in the domestic allocation of CO2 emissions quotas, and (iii) the non-cooperative behavior of countries and regions in the burden sharing of CO2 concentration stabilization. The two topics of endogenous learning and game-theoretic approach to economic modeling are two manifestations of the need to take into account the strategic behavior of agents in the evaluation of climate change policies. In the first case an R&D policy or a demonstration and deployment (D&D) program are put in place in order to attain a cost reduction through the learning effect; in the second case the agents (countries) reply optimally to the actions decided by the other agents by exploiting their strategic advantages. Simulations based on integrated assessment models illustrate the approaches. These studies have been conducted under the Swiss NCCR-Climate progra

Exploiting Domain Knowledge in Making Delegation Decisions

@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin

Replication strategies and the evolution of cooperation by exploitation

Introducing the concept of replication strategies this paper studies the evolution of cooperation in populations of agents whose offspring follow a social strategy that is determined by a parent's replication strategy. Importantly, social and replication strategies may differ, thus allowing parents to construct their own social niche, defined by the behaviour of their offspring. We analyse the co-evolution of social and replication strategies in well-mixed and spatial populations. In well-mixed populations, cooperation-supporting equilibria can only exist if the transmission processes of social strategies and replication strategies are completely separate. In space, cooperation can evolve without complete separation of the timescales at which both strategy traits are propagated. Cooperation then evolves through the presence of offspring exploiting defectors whose presence and spatial arrangement can shield clusters of pure cooperators

Exploiting tacit knowledge through knowledge management technologies

The purpose of this paper is to examine the contributions and suitability of the available knowledge management (KM) technologies, including the Web 2.0 for exploiting tacit knowledge. It proposes an integrated framework for extracting tacit knowledge in organisations, which includes Web 2.0 technologies, KM tools, organisational learning (OL) and Community of Practice (CoP). It reviews a comprehensive literature covering overview of KM theories, KM technologies and OL and identifies the current state of knowledge relating to tacit knowledge exploitation. The outcomes of the paper indicate that Internet and Web 2.0 technologies have stunning prospects for creating learning communities where tacit knowledge can be extracted from people. The author recommends that organisations should design procedures and embed them in their Web 2.0 collaborative platforms persuading employees to record their ideas and share them with other members. It is also recommended that no idea should be taken for granted in a learning community where tacit knowledge exploitation is pursued. It is envisaged that future research should adopt empirical approach involving Complex Adaptive Model for Tacit Knowledge Exploitation (CAMTaKE) and the Theory of Deferred Action in examining the effectiveness of KM technologies including Web 2.0 tools for tacit knowledge exploitation

Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

An important challenge for safety in machine learning and artificial intelligence systems is a~set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive Computing, Special Issue "Artificial Superintelligence: Coordination & Strategy

Using motivation derived from computer gaming in the context of computer based instruction

This paper was originally presented at the IEEE Technically Sponsored SAI Computing Conference 2016, London, 13-15 July 2016. Abstract— this paper explores how to exploit game based motivation as a way to promote engagement in computer-based instruction, and in particular in online learning interaction. The paper explores the human psychology of gaming and how this can be applied to learning, the computer mechanics of media presentation, affordances and possibilities, and the emerging interaction of playing games and how this itself can provide a pedagogical scaffolding to learning. In doing so the paper focuses on four aspects of Game Based Motivation and how it may be used; (i) the game player’s perception; (ii) the game designers’ model of how to motivate; (iii) team aspects and social interaction as a motivating factor; (iv) psychological models of motivation. This includes the increasing social nature of computer interaction. The paper concludes with a manifesto for exploiting game based motivation in learning