Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

Fiscal Policy and Inflation Targets: Does Credibility Matters?

We reconsider Svensson’s inflation-targeting proposal in a model where the need to raise seigniorage revenues determines the socially optimal inflation rate and distortionary taxes cause the inflation bias. Interpreting the targets as contracts, we show that the interaction between fiscal and monetary policy complicates the structure of the optimal contract. Moreover, if the commitment technology is imperfect, «highish» targets generate lower inflation than targets, which are too low to be credible. Alternatively, interpreting inflation targets as policy delegation to a non-distortionary target-conservative agent, we show that target-conservative bankers are public-expenditures conservative. Unfortunately, only idiosyncratic views about the benefits from public expenditures can be invoked to justify expenditures-conservatism, implying that target-conservative agents are also weight-conservative.Central bank independence, Inflation targets

Heidegger and the Questionability of the Ethical

Despite Heidegger’s critique of ethics, his use of ethically-inflected language intimates an interpretive ethics of encounter involving self-interpreting agents in their hermeneutical context and the formal indication of factical life as a situated dwelling open to possibilities enacted through practices of care, interpretation, and individuation. Existence is constituted practically in Dasein’s addressing, encountering, and responding to itself, others, and its world. Unlike rule-based or virtue ethics, this ethos of responsive encounter and individuating confrontation challenges any grounding in a determinate or exemplary model of reason, human nature, the virtues, or tradition

Existence and uniqueness for Mean Field Games with state constraints

In this paper, we study deterministic mean field games for agents who operate in a bounded domain. In this case, the existence and uniqueness of Nash equilibria cannot be deduced as for unrestricted state space because, for a large set of initial conditions, the uniqueness of the solution to the associated minimization problem is no longer guaranteed. We attack the problem by interpreting equilibria as measures in a space of arcs. In such a relaxed environment the existence of solutions follows by set-valued fixed point arguments. Then, we give a uniqueness result for such equilibria under a classical monotonicity assumption

Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning

The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications. Therefore, interpreting and explaining RL agents have been active research topics in recent years. Existing methods for post-hoc explanations usually adopt the action matching principle to enable an easy understanding of vision-based RL agents. In this paper, it is argued that the commonly used action matching principle is more like an explanation of deep neural networks (DNNs) than the interpretation of RL agents. It may lead to irrelevant or misplaced feature attribution when different DNNs' outputs lead to the same rewards or different rewards result from the same outputs. Therefore, we propose to consider rewards, the essential objective of RL agents, as the essential objective of interpreting RL agents as well. To ensure reward consistency during interpretable feature discovery, a novel framework (RL interpreting RL, denoted as RL-in-RL) is proposed to solve the gradient disconnection from actions to rewards. We verify and evaluate our method on the Atari 2600 games as well as Duckietown, a challenging self-driving car simulator environment. The results show that our method manages to keep reward (or return) consistency and achieves high-quality feature attribution. Further, a series of analytical experiments validate our assumption of the action matching principle's limitations

Games of Partial Information and Predicates of Personal Taste

A predicate of personal taste occurring in a sentence in which the perspectival information is not linguistically articulated by an experiencer phrase may have two different readings. In case the speaker of a bare sentence formed with a predicate of personal taste uses the subjective predicate encoding perspectival information in one way and the hearer interprets it in another way, the agents’ acts are not coordinated. In this paper I offer an answer to the question of how a hearer can strategically interact with a speaker on the intended perspectival information so that both agents can optimally solve their coordination problem. In this sense, I offer a game-theoretical account of the strategic communication with expressions referring to agents’ perspectives, communication which involves the interaction between a speaker who intends to convey some perspectival information and who chooses to utter a bare sentence formed with a predicate of personal taste, instead of a sentence in which the perspectival information is linguistically articulated by an experiencer phrase, and a hearer who has to choose between interpreting the uttered sentence in conformity with the speaker’s autocentric use of the predicate of personal taste or in conformity with the speaker’s exocentric use

Data-driven modeling of collaboration networks: A cross-domain analysis

We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and science, specifically 300.000 co-authorship relations between 95.000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.Comment: 25 pages, 13 figures, 4 table