1/ Four 5-yr posts starting Jan ‘25: (Links @ thread end) 1. Postdoc (PD): Soc. Psyc or related DL:1-Oct-24 2. PD comp./mathematical modelling DL:1-Oct-24 3. PD: Research Management (0.5FTE) DL:07-Oct-24 4. Asst. Prof: Soc Psych. & Innovative Methods DL 27-Sep-24 @polpsyispp.bsky.social#jobfairy
10/ links: Postdoc(PD): Soc. Psyc/related DL: 1-Oct-24 tinyurl.com/ERC-PD1tinyurl.com/ERC-PD2tinyurl.com/ERC-RMMtinyurl.com/ERC-AP
9/ None of these ideas are new (cf. stereotypes, schemas, heuristics, etc.) - but compression is a mathematical framework for making sense of it all in a new (& hopefully more integrated) way. Pls get in touch if you have any questions; and of course please retweet if you’re comfortable with that.
8/ If successful, ID-Compression will provide a unifying theory, linking basic psychology to macro social structure allowing us to understand, detect, quantify and (maybe) manage polarization.
7/ People try to act comprehensibly in a given information system, thereby sustaining it. For example, an American wearing a red cap could look like a MAGA Republican, so Democrats avoid wearing red caps, amplifying the signal.
6/ Identity compression produces information spaces where people can recognize friend or foe with minimal data, since the identity-information relations are highly redundant.
5/ We hypothesize that polarization exists when socially structured attributes align with group identities to create a compressible identity information space where knowing someone’s group identity allows you to guess related attributes. Conversely, knowing some attributes tells you their identity.
4/ Take a social example: in these Serbian protests, multiple attitudes like “sex before marriage is OK,” “men can love men,” or “God should be obeyed” are compressible as a single dichotomy – RELIGIOUS or LIBERAL: if you knew someone’s answer to one, you could guess them all.
3/ ID-Compression builds on simple idea: that the link between social identity & polarization is compressibility. Compressibility exists when there's redundant information. e.g. we can capture key features of this painting with a fraction of the data, because a lot of information is redundant.
