Living with George Eliot: A Tribute to my Parents

It was a strange but much-appreciated honour to be asked to address the annual George Eliot Fellowship lunch in 2016. It was strange because I\u27d attended many such events long ago - fewer more recently - and had always sat with the \u27rank and file\u27 on the lower tables while Mum and Dad, officers and distinguished guests occupied the top table. So, when on Sunday 20 November, I rose to speak, I was quite miffed to find that there was no top table! All very egalitarian, but it left me wandering up and down the floor as I imparted my ramblings! The author of this article is the son of Kathleen and Bill Harris

Who Dislikes Whom? Affective Polarization between Pairs of Parties in Western Democracies

While dislike of opposing parties, that is, affective polarization, is a defining feature of contemporary politics, research on this topic largely centers on the United States. We introduce an approach that analyzes affective polarization between pairs of parties, bridging the US two-party system and multiparty systems in other democracies. Analyzing survey data from twenty Western democracies since the mid-1990s, first, we show that partisans' dislike of out-parties is linked to elite policy disagreements on economic issues and, increasingly over time, also to cultural issues. Secondly, we argue and empirically demonstrate that governing coalition partners in parliamentary democracies display much warmer feelings toward each other than we would expect based on elite policy (dis)agreements. Third, we show that radical right parties are disliked much more intensely than we would expect based on policy disputes and coalition arrangements. These findings highlight the policy-based and institutional underpinnings of affective polarization

I Don\u27t Want A Million Dollars

https://digitalcommons.library.umaine.edu/mmb-vp/3166/thumbnail.jp

Don\u27t Send The Little Ones Crying To Bed

https://digitalcommons.library.umaine.edu/mmb-vp/1328/thumbnail.jp

I Wonder Who\u27s Kissing Her Now

VERSE 1You have loved lots of girls in the sweet long agoAnd each one has meant Heaven to you,You have vowed your affection to each one in turnAnd have sworn to them all you’d be true;You have kissed ‘neath the moon while the world seemed in tune,Then you’ve left her to hunt a new game,Does it ever occur to you later my boy,That she’s probably doing the same? CHORUSI wonder who’s kissing her now,Wonder who’s teaching her now,Wonder who’s looking into her eyesBreathing sighs, telling lies;I wonder who’s buying the wine,For lips that I used to call mine,Wonder if she ever tells him of me,I wonder who’s kissing her now.I kissing her now VERSE 2If you want to feel wretched and lonely and blue,Just imagine the girl you love bestIn the arms of some fellow who’s stealing a kissFrom the lips that you once fondly pressed;But the world moves a pace and the loves of todayFlit away with a smile and a tear,So you never can tell who is kissing her now,Or just whom you’ll be kissing next year. CHORU

The Sun that Shines On Dixieland

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Retrospective Inference as a Form of Bounded Rationality, and Its Beneficial Influence on Learning

Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious, but, in general, leads to suboptimal beliefs about past states, since it ignores the fact that new observations typically contain information about the past as well as the present. This is disadvantageous both because knowledge of past states may be intrinsically valuable, and because it impairs learning about fixed or slowly changing parameters of the environment. For these reasons, in offline data analysis it is usual to infer on every set of states using the entire time series of observations, an approach known as (fixed-interval) Bayesian smoothing. Unfortunately, however, this is impractical for real agents, since it requires the maintenance and updating of beliefs about an ever-growing set of states. We propose an intermediate approach, finite retrospective inference (FRI), in which agents perform update beliefs about a limited number of past states (Formally, this represents online fixed-lag smoothing with a sliding window). This can be seen as a form of bounded rationality in which agents seek to optimize the accuracy of their beliefs subject to computational and other resource costs. We show through simulation that this approach has the capacity to significantly increase the accuracy of both inference and learning, using a simple variational scheme applied to both randomly generated Hidden Markov models (HMMs), and a specific application of the HMM, in the form of the widely used probabilistic reversal task. Our proposal thus constitutes a theoretical contribution to normative accounts of bounded rationality, which makes testable empirical predictions that can be explored in future work

Same Old Moon

https://digitalcommons.library.umaine.edu/mmb-vp/6452/thumbnail.jp

Thursday is my Jonah Day

https://digitalcommons.library.umaine.edu/mmb-vp/6430/thumbnail.jp