We have proposed a metaphor “DAnger Susceptible daTa codON” (DASTON) in data subject to processing by Danger Theory (DT) based Artificial Immune System (DAIS). The DASTONs are data chunks or data point sets that actively take part to produce “danger”; here we abstract “danger” as required outcome. To have closer look to the metaphor, this paper furthers biological abstractions for DASTON. Susce...

A primary challenge from the relativist to the contextualist about epistemic modals is to explain eavesdropping data—i.e., why the eavesdropper is inclined to judge the speaker as having uttered an epistemic modal falsehood (when she is so inclined), even though the speaker’s utterance is true according to reasonable contextualist truth conditions. The issue turns in large part on the strength ...

Over the last decade, a new idea challenging the classical self-non-self viewpoint has become popular amongst immunologists. It is called the Danger Theory. In this conceptual paper, we look at this theory from the perspective of Artificial Immune System practitioners. An overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world. A n...

Signal detection theory (SDT) has been used to model optimal stimulus discrimination for more than four decades in evolutionary ecology. A popular standard that maximizes payoff per encounter was recently criticized being too simplistic, leading erroneous predictions. We review a number of SDT models have received less attention but explicitly taken repeated encounters into account, focusing on...