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Vad heter du? Den frågan har du fått många gånger.

Men varför heter du? Den frågan har du kanske inte tänkt på så ofta. Hur kommer det sig att du över huvud taget har ett namn, och varför ser det ut som det gör? Det vill Sverker Johansson berätta om i denna underhållande och tanke­väckande bok. Med blick för såväl de stora utvecklings­linjerna som för de kuriösa exemplen rör han sig mellan alla tider och platser för att fånga in namntraditionernas mångfald.

Beroende på var vi hamnar i tid och rum kan ett för­namn bestämmas utifrån veckodag, förfäder, vilka onda andar som ska luras eller vad modern ser när barnet föds. Förutom förnamn kan vi ha efternamn – men också föräld­ranamn, gårdsnamn, bynamn, barnnamn, kastnamn, mel­lannamn, generationsnamn eller tillnamn baserade på allt från lyten via yrken till tv-program. Visste du till exempel att Caligula betyder barnstövlar, att John är det vanligaste förnamnet på Wikipedia, att delfiner har namnmelodier och att Mao Zedongs mellannamn Ze kommer från en dikt som hans släkt i fjorton generationer hämtade namn ur?

Mycket pekar på att vi bär namn inte så mycket för att kunna tilltalas som för att kunna omtalas…

This entry departs from promissory imaginaries about educational technology to show how postdigital research adopts a critical stance on futures. Futures are observed as both a research object and a methodological frame to understand an increasingly complex and technology-dense reality. Temporalities are identified as follows: futures in the present, near futures, far futures, and alternative futures. These situated time-spaces cater for different methodological approaches. Anticipatory methods are elaborated in relation to a near future, while far futures can be grasped through speculatively methods. Alternative futures are usually formulated in the literary vein of science fiction. The tendency in recent futures studies is to make explicit dimensions of ethics, care, and values in the crafting of educational futures.

Languages change over time, due to various processes that likely have been operative since the dawn of language. But our understanding of the relative importance of different processes in the distant past remains limited. Methods for reconstructing language change are hampered by shortage of training data.

Simulating language change in software can help, testing processes and producing simulated language data as input for reconstruction tests. In simulation, the processes are known and controllable, and the true diversification path is known. Tuning process strength in simulation until the results resemble real language diversity may inform theories of language dynamics. 

But simulated data will only be helpful if the simulation reproduces relevant aspects of reality closely enough. Several items in List (2019) Open problems in computational linguistics concern simulation issues. Extant simulations are mainly of two types: 

• Detailed short-term simulations of within-language dynamics, often agent-based (e.g. Nolfi & Mirolli, 2010).
• Macro-scale long-term simulations, but with linguistic and/or geographical details abstracted away (e.g. Wichmann, 2017; Kapur & Rogers, 2020).

Neither type covers the middle ground where within-language and between-language dynamics meet. This work aims to fill that gap, with a simulation that has sufficient linguistic, geographic and anthropological detail to produce realistic data, and sufficient scope to cover macro-scale dynamics over millennia.

The basic simulation unit is a speech community with typically 100-1000 speakers, speaking a common language. Their language has an explicit vocabulary with word-forms and meanings. Real languages from CLICS3 (Rzymski et al., 2019) are used as seed languages, which then evolve through regular sound change, word gain and loss, semantic shift, language contact, and areal effects. All processes are adjustable and can be disabled.

The geography of the real world is used, with topography from De Ferranti (2015), rivers from Kelso (2016) and climate/ecology from NASA (2016). Each speech community lives in a 50x50 km grid square, which may be shared with other communities up to a carrying capacity. Population may increase or decrease depending on food availability, and surplus population may migrate to greener pastures, forming a new community whose language then evolves independently. Travel depends on real terrain and available technology (innovations occur occasionally, starting from paleolithic level).

Simulation results are available as Swadesh matrices, or in formats suitable for automated reconstruction such as CLDF or NEXUS. True trees and true cognate sets are saved separately.

Software and sample output available at https://github.com/[ANONYMIZED]/LangChangeSimulator/tree/master 

De Ferranti, J. (2015) Viewfinder Panoramas Digital Elevation Model. http://www.viewfinderpanoramas.org/dem3.html 

Kapur, R & Rogers, P (2020) Modeling language evolution and feature dynamics in a realistic geographic environment. In Proceedings of the 28th International Conference on Computational Linguistics, Barcelona.

Kelso, N V (2016) Natural Earth Data. https://www.naturalearthdata.com/downloads/ 

List, Johann-Mattis (2019): Open problems in computational historical linguistics. Invited talk presented at the 24th International Conference of Historical Linguistics (2019-07-01/05, Canberra, Australian National University).

NASA (2016) NASA Earth Observations. https://neo.gsfc.nasa.gov/ 

Nolfi, S & Mirolli, M (2010) Evolution of Communication and Language in Embodied Agents. Springer.

Rzymski, Christoph and Tresoldi, Tiago et al. 2019. The Database of Cross-Linguistic Colexifications, reproducible analysis of cross- linguistic polysemies. DOI: 10.1038/s41597-019-0341-x

Wichmann, S. (2017) Modeling language family expansions. Diachronica 34:1, 79-101.

This chapter aims to describe how cultures have emerged in interactions among users of the multitude of online platforms that have become available over the past few decades. It discusses innovations regarding uses of representations to communicate identity, time, and space in social practices with technology, and how cybercultures are played out in theory and in practice. Cybercultures resemble cultures in the non-virtual world—but display significant differences regarding social rules, identity, and spatiotemporal issues. Case studies of three types of cybercultures in social media: information and knowledge building on Wikipedia, culture, and virtual world building on Second Life, and dating practices on online dating services, such as Tinder, will shed light on how cyberspace allows for developing both symbolic representations and social practices through computer-mediated communication (CMC), and how users are situated in the continuum virtual-real.

When we talk about protolanguage, it is typically in the singular. The name of this conference series is Exhibit A here, but also e.g. the authoritative Oxford Handbook of Language Evolution talks about “the protolanguage” (Tallerman, 2012, my emphasis). This leads us to imagine a single protolanguage being spoken (or signed or mimed or whatever) at some point in prehistory. At most we imagine multiple successive stages of more and more advanced protolanguages (e.g. Jackendoff & Wittenberg, 2014, Author, submitted a). 

But rarely has the likely diversity of protolanguages been considered. A literature search for “protolanguages” in the plural mainly returns results referring to the proto-forms ancestral to modern language families (proto-Indoeuropean, etc.); see Robbeets et al. (2020) for an example. There are rare exceptions referring to plural protolanguages in the sense used at this conference (e,g, Donald, 1999, Dowman, 2008) but they still do not have diversity in focus.

Is there any reason to believe that linguistic diversity in the world was any less in prehistory than it is today? The answer to that question depends on whether the forces driving linguistic diversification were operative already at the protolinguistic stage. If two groups speaking the same language get separated, their dialects will gradually drift apart over time until mutual intelligibility is lost, the linguistic equivalent of genetic drift in biology; this must have been operative for as long as we had conventionalized language at all. Differential language contact can also cause language change, with different subgroups accumulating different loanwords etc. In this way, diversity begets more diversity, and this would have been operative as soon as some diversity was present. Finally, if language is used as a marker of group identity, a group may intentionally steer their way of speaking away from the neighbors’. This requires a level of meta-linguistic awareness that need not have been present from the beginning. 

But even without the latter force, linguistic drift and language contact would have been quite sufficient to drive diversity among protolanguages. And even in the absence of synchronic diversity, linguistic drift would create diachronic diversity: over a millennium or so, a modern language changes beyond intelligibility, and across 10,000 years it changes beyond recognition even at the family level. Even if we assume for the sake of the argument that protolanguage was a single lineage, every 1,000 years what is spoken deserves to be called a new language.

A single lineage is, however, extremely unlikely. From the dawn of language, people have spread out over multiple continents, far out of touch with each other. Protolanguages must have diverged. But populations were sparse, estimates for world population at relevant times are typically below one million, sometimes well below (e.g. Huff et al. 2010), living in small tribes. From such numbers, a rough order-of-magnitude estimate of the total number of protolanguages ever spoken can be calculated (Author, submitted b). With any reasonable assumptions, the number is huge, much larger than current linguistic diversity.

Why do humans have language at all and how did we become language users? These are central questions in language evolution, but no general consensus exists on the answers, nor even on what methods to use to find answers. This is a complex topic that requires input from many disciplines, including, but not limited to, linguistics, evolutionary biology, palaeoanthropology, neurobiology, archaeology, cognitive science, and primatology. Nobody is an expert in all these areas, and experts in one area sometimes overlook needed input from other areas. Consensus does not even exist among linguists on what language is—opinions range from the physical speech acts themselves to language as an abstract social communication system to language as computational machinery in the individual and to language as an innate species-defining, genetically encoded capacity of humans. These different views of language imply very different evolutionary explanations. At the same time, all of these perspectives have some validity; the speech acts do occur, language use does take place in a social context, the individual language user does somehow produce and parse sentences, and human babies are born with a predisposition for language learning that ape babies lack. The disagreements are mainly a matter of emphasis, namely which aspects are regarded as of primary interest, requiring explanation. The preeminent linguist of the early 20th century, Ferdinand de Saussure, focused on the first two perspectives with his distinction between parole (speech acts) and langue (the social system). The preeminent linguist of the late 20th century, Noam Chomsky, focuses instead on the latter two, especially the computational machinery, and he regards the first two as not worthy of a linguist’s attention. But neither focus is adequate on its own; a viable theory of language evolution must be able to explain all aspects of language, notably both the evolution of the language capacity that resides in each human brain and the evolution of the human social context in which language is used. No generally accepted theory exists today. Instead of a single accepted theory, the field of language evolution is awash with a multitude of different models, scenarios, and hypotheses about how things might have happened. To make matters worse, there is something of a paradigm split in the study of language origins. The split is largely along the line between Saussure and Chomsky mentioned above. To put it simply, those researchers who use the label “biolinguistics” try to explain the origin of Chomsky’s computational machinery (see Biolinguistics) whereas most work on language evolution is concerned with explaining the origins of Saussure’s langue, language as a social system; the latter is here called “mainstream evolutionary linguistics.” Language evolution is not, however, about the origin of individual languages (English, Chinese, etc.). Sometimes “language evolution” is used to refer to diachronic language change in recent times, as studied by historical linguists, and an evolutionary perspective can indeed be fruitful in this area. But this article does not cover that kind of language evolution, except peripherally in Cultural Evolution.

Chomsky (e.g. 2010) and others regard unlimited Merge as the defining feature of language, that cannot evolve gradually. The neural implementation of Merge is not well understood (Rizzi 2012, Zaccarella et al 2017), but must involve something functionally equivalent to pointers in working memory. Every Merge requires two pointers, and full syntactic trees may require dozens. Other syntactic paradigms also need pointers.

Humans do hierarchies in general better than chimpanzees. Any hierarchical thinking requires nested pointers in working memory, but they are neurologically expensive and degrade with depth (Crawford et al. 2016). Humans have larger working-memory capacity than chimpanzees, which has been proposed as key to human cognitive evolution (Read 2008, Coolidge & Wynn, 2005). Gradual evolutionary growth of pointer capacity will allow gradually increasing syntactic complexity, without saltations in the underlying computational machinery. Both depth degradation and pointer capacity naturally limit Merge even in modern humans, consistent with corpus data (e.g. Karlsson 2010).

Chomsky (e.g. 2010) and others regard unlimited Merge as the defining feature of language, that cannot evolve gradually. The neural implementation of Merge is not well understood (Rizzi 2012, Zaccarella et al 2017), but must involve something functionally equivalent to pointers in working memory. Every Merge requires two pointers, and full syntactic trees may require dozens. Other syntactic paradigms also need pointers.

Humans do hierarchies in general better than chimpanzees. Any hierarchical thinking requires nested pointers in working memory, but they are neurologically expensive and degrade with depth (Crawford et al. 2016). Humans have larger working-memory capacity than chimpanzees, which has been proposed as key to human cognitive evolution (Read 2008, Coolidge & Wynn, 2005). Gradual evolutionary growth of pointer capacity will allow gradually increasing syntactic complexity, without saltations in the underlying computational machinery. Both depth degradation and pointer capacity naturally limit Merge even in modern humans, consistent with corpus data (e.g. Karlsson 2010).

Chomsky, Noam. (2010). Some simple evo devo theses: how true might they be for language? In Richard K Larson, Viviane Déprez, & Hiroko Yamakido (Eds.), The Evolution of Human Language. Biolinguistic Perspectives. Cambridge: Cambridge University Press.

Coolidge, Frederick L & Wynn, Thomas (2005) Working memory, its executive functions, and the emergence of modern thinking. Cambridge Archaeological Journal 15:5-26.

Crawford, Eric & Gingerich, Matthew & Eliasmith, Chris (2016) Biologically plausible, human-scale knowledge representation. Cognitive Science 40:782-821.

Karlsson, Fred (2010) Syntactic recursion and iteration. In Harry van der Hulst, ed., Recursion and Human Language. Berlin/New York: Mouton de Gruyter,

Read, Dwight W (2008) Working memory: A cognitive limit to non-human primate recursive thinking prior to hominid evolution. Evolutionary Psychology 6:676-714.

Rizzi, Luigi (2012) Core linguistic computations: How are they expressed in the mind/brain? Journal of Neurolinguistics 25:489-499.

Zaccarella et al (2017) Building by syntax: the neural basis of minimal linguistic structures. Cerebral Cortex 27:411-421.