Harvey Whitehouse

Ritual and social evolution: Understanding social complexity through data

. Seshat is the Egyptian deity of writing, knowledge and wisdom and her name literally translated means "she who scribes". It could be said that Seshat was the first goddess of databases and so we have borrowed her name for our global history databank. The aim here is to show how Seshat can be used to test theories about the evolution of social complexity. First we will consider how such theories can be inspired by qualitative observation of patterns in the social world. For the purposes of illustration we will consider the theory of "modes of religiosity". We will examine efforts to test this theory against data on real world groups, past and present. Finally we will consider how this theory can be tested in a more rigorous and systematic way using Seshat: Global History Databank.

Introducing Seshat

Much is known about past societies but it is all scattered" and unavailable in its current form in texts and the minds of many thousands of historians and archaeologists and cannot be readily analysed using statistical tools. Even historical materials stored electronically are not organized in a way that can be used to test theories. Seshat will allow us to connect facts about the past spatially, temporally, thematically, and conceptually. We will be able to use Seshat to test theories about cultural evolution, discarding those that turn out to be false. At the same time we can use Seshat to analyse patterns within societies (e.g. looking at how social complexity compares with variables on ritual and warfare) as well as across them.

Eventually we want Seshat to contain extensive data on every single society ever documented on the face of the earth. But we must be realistic and so our initial goal is to sample past societies selectively. We have chosen thirty regions spread around the world to serve as a sampling scheme and for each of these regions we capture details of polities that rose and fell between the Neolithic and industrial revolution. "World Sample 30" as it has become known was created by first dividing the world into ten major regions and then choosing three "Natural Geographic Areas" (NGAs) from each of them. Each NGA represents an area with some ecological unity, roughly 100km x 100km in size (although NGAs can be somewhat larger or smaller than that). The three NGAs selected for each world region are ones that developed social complexity very early (like Upper Egypt), very late (like Iceland) or somewhere in the middle (like Chuuk). The purpose of stratifying the sample in this way is to maximize variance in the data. But obviously this is all relative -so the most complex NGA in Oceania-Australia is Big Island Hawaii which is nothing like as complex as (say) Latium.

For each NGA we code data pertaining to polities (e.g. states like Egypt's classic old kingdom) or quasi-polities (e.g. sub-traditions) as far back as the data will allow. This means for some NGAs we only have a few time slices whereas for Upper Egypt we have about 40 or so.

The process of populating the database involves a well-established division of labour. RAs code the data initially and then gather expert input. The data aren't static -new insights be incorporated. In other words we do not freeze data or its interpretation forever. We also capture uncertainty and disagreement -we do not want to 'paper over' these crucial aspects of humanities research. Each item of information coded in Seshat is not a simple datapoint. Even seemingly simple facts like the size of the largest settlement in Egypt's New Kingdom Ramasside period (to take a simple example) is actually a conglomerate of information with a complex structure. We can handle uncertainty by including population ranges and other estimates (we never try to go beyond what the current state of knowledge allows). Each piece of information is linked by a numbered footnote to a source. This information is vetted by experts -and every time it is updated or changed this action is recorded.

The theory of modes of religiosity

Seshat has been designed to test theories about the evolution of social complexity (http://seshatdatabank.info). But where do such theories come from in the first place? Here we consider the example of one such theory, concerning modes of religiosity (or simply the 'modes theory'). The modes theory predicts that collective rituals tend to fall into two main types: doctrinal and imagistic [START_REF] Whitehouse | Inside the Cult: religious innovation and transmission in Papua New Guinea[END_REF][START_REF] Whitehouse | Arguments and Icons: divergent modes of religiosity[END_REF][START_REF] Whitehouse | Modes of Religiosity: a cognitive theory of religious transmission[END_REF].

A hallmark feature of the doctrinal mode of religiosity is that the main teachings and practices are frequently repeated. Think for example of Sunday Church services or Friday prayers at the Mosque). These kinds of routinized rituals are typically accompanied by sermonizing, in which narratives and creeds are repeatedly rehearsed and memorized. So it is easy to spot unauthorized innovations so as to produce a stable and shared body of doctrine. Doctrinal practices tend to spread rapidly and become standardized across much larger populations. Religious hierarchies typically become established enforcing the orthodoxy and punishing heresy. The frequent repetition of doctrinal rituals -from daily prayers to weekly Holy Days through to all the events that fill up religious calendars -serve to cement the social identity of potentially enormous social groups, sometimes even on a global scale.

The imagistic mode, on the other hand, is based around rarely performed rituals involving much higher levels of emotional intensity (e.g. painful initiation rituals or gruelling tests of stamina and self-deprivation). These ritualized ordeals trigger personal reflection on the meaning and symbolism of the acts and artefacts involved, but are not necessarily linked to a religious orthodoxy of any kind. Imagistic practices are very effective at binding local networks of people into tightly knit, emotionally bonded communities. They seem to create a sense of family connection based, not on bonds of kinship and descent, but on the sharing of life-changing ritual experiences. On they other hand they don't spread very efficiently -passing on the tradition requires the entire ritual community to come together (unlike doctrinal practices that can be passed on by word of mouth or the spread of holy books). This means that imagistic groups tend to be localized and uncentralized. Now, underlying the two modes are two distinct ways of remembering.

In the case of the doctrinal mode, belonging to the community basically means sharing a set of group-defining beliefs and practices. These beliefs and practices will have been socially learned from other people in the group and many of them are regularly repeated and stored as part of the person's general knowledge about the world. Psychologists refer to this kind of knowledge as 'semantic memory' [START_REF] Tulving | Elements of Episodic Memory[END_REF]. A good example is the general fact that Elisabeth II is the Queen of England. This is one of countless facts that every normal person in the UK, above a certain age, simply knows. Not knowing this kind of thing would arouse suspicions that the person in question is not 'one of us'.

A great deal of semantic knowledge consists of beliefs and behaviours that collectively -that is, taken together as an entire cultural system -demonstrate membership of the group. But cultural knowledge of this kind is also very impersonal. When information is stored in semantic memory it is more or less disconnected from time and space. For example, who can remember where or when they first learned who is currently the queen of England? It is just something we know -and although there must have been a time when that information was new to us, these personally salient details have typically fallen away; it is just a generic fact rather than a part of our personal, lived experience. Moreover, it is impersonal in another way as well -most of the content of semantic memory is acquired from others via social learning. We had to be told that Elizabeth II is the queen of England; it is not a conclusion we reached through some independent process of discover.

Doctrinal religions basically consist of beliefs and practices that are stored in semantic memory [START_REF] Whitehouse | Modes of Religiosity: a cognitive theory of religious transmission[END_REF]. We can think of a religious orthodoxy as a great corpus of things you are supposed to do and say in order to be an upstanding member of the religious community. But because of the way these beliefs and practices are processed in memory, they are essentially also impersonal. When our religious identities are made salient our personal selves become less so, and vice-versa. Group psychologists refer to this as 'identification' [START_REF] Mael | Alumni and their alma maters: A partial test of the reformulated model of organizational identification[END_REF][START_REF] Tajfel | The social identity theory of intergroup behavior[END_REF] -and over several decades the field of 'Social Identity Theory' suggests that this form of group alignment is essentially depersonalizing. When our semantic memory for groupdefining beliefs is activated we are less conscious of our own personal identities.

In the case of imagistic practices, group alignment comes about in a very different way from a cognitive perspective. In particular, the types of memories that matter are firmly rooted in one's personal life history. Psychologists refer to these as 'episodic memories' because they specify the details of a particular moment or sequence of events in lived experience [START_REF] Tulving | Elements of Episodic Memory[END_REF]. Some of these memories are so emotionally intense, unique, and consequential that we can never forget them. Typically, the most long-lived of these memories are for dysphoric experiences -such as painful or frightening episodes [START_REF] Conway | Autobiographical knowledge and autobiographical memories[END_REF]. These rare but very intense experiences shape the personal self -we call them 'transformative' or 'self-shaping' episodes [START_REF] Singer | The Remembered Self: Emotion and memory in personality[END_REF]. In the case of dysphoric rituals, such as painful initiations, these can trigger a great deal of reflection on their significance and meaning. And when such experiences are felt to be shared with other members of the group this produces a visceral sense of oneness, known as identity fusion [START_REF] Swann | When Group Membership Gets Personal: A theory of identity fusion[END_REF][START_REF] Jimnez | The Dynamic Identity Fusion Index (DIFI): A new continuous measure of identity fusion for Web-based questionnaires[END_REF].

Identity fusion is unlike identification in that it taps directly into the personal self. In the case of the doctrinal mode, making the group salient makes the personal self less so -but it's quite the opposite with the imagistic mode. People who have shared the same self-shaping experiences regard the group and the self as functionally equivalent -if you make the group salient you activate the personal self-concept and vice-versa. So the idea here is that imagistic rituals fuse participants to the group -creating in effect an extremely powerful form of social cohesion capable of motivating quite extreme forms of pro-group action, including willingness to fight and die for the group.

For some years now we have been studying the psychological effects of these two kinds of group bonding -doctrinal and imagistic -in a wide range of cultural groups. As well as studying religious adherents, we have also been studying a much wider range of groups: football fans, soldiers, university fraternities, revolutionaries, and tribal warriors [START_REF] Whitehouse | Brothers in Arms: Libyan revolutionaries bond like Family[END_REF][START_REF] Whitehouse | The evolution of extreme cooperation via intense shared experiences[END_REF]. And we find the same pattern in all these different contexts: high-frequency/low arousal rituals give rise to identification and other doctrinal features; low-frequency/high arousal rituals give rise to fusion and other imagistic features [START_REF] Whitehouse | The Ties that Bind Us: Ritual, fusion, and identification[END_REF].

Modes of Religiosity over Time and Space

Modes of religiosity are manifested in bounded regions during relatively discrete time periods -and so if we want to observe how they wax and wane we need to study the histories of various groups. This is something scholars have been doing for some time [START_REF]Theorizing Religions Past: Archaeology, History, and Cognition[END_REF][START_REF]Graeco-Roman Religions and the Cognitive Science of Religion[END_REF][START_REF] Larson | Understand Greek Religion: A cognitive approach[END_REF]. An instructive example is the way historian Anne Clark has discussed evidence of modes dynamics in late medieval English monasteries [START_REF] Clark | Testing the Two Modes Theory: Christian Practice in the Later Middle Ages[END_REF].

On the one hand, Clark found extensive evidence for the doctrinal mode of religiosity: routinized transmission, heavy emphasis on verbal and textual codification of a religious orthodoxy, coupled with expansionary, centralizing, and hierarchical dynamics. But on the other hand, Clark also pointed to evidence of monastic practices that had a more imagistic character, for example in the form of visions and other extraordinary interactions with the divine. Although rare in the lifetimes of individuals such experiences were widespread aspects of religious life. But unlike truly imagistic practices, the visions and visitations Clark described in the monasteries tended to be uniquely personal and solitary rather than part of any kind of collective ritual. As such these experiences were incapable of generating the distinctive patterns of localised group alignment and cohesion diagnostic of the imagistic mode.

Clark's careful descriptions of medieval religious practices provided new insights into the way doctrinal religions can co-opt certain kinds of personal experience in building commitment to mainstream beliefs and practices. Case studies like these have undoubtedly been valuable not only in exploring the applicability of the modes theory to historical materials but in extending the reach of the theory to consider a diversity of ways in which doctrinal and imagistic dynamics have had to adapt to the conditions typical of different regions and historical periods.

Nevertheless, an obvious problem with these kinds of case studies is that of self-selection: were cases studies more likely to be chosen because they supported the modes theory? Note that bias of this kind does not even need to be deliberate. Perhaps some scholars were interested in the theory because it resonated with their data, maybe prompting questions and criticisms as well as confirmatory evidence, but in a way that nevertheless skewed the sample of available case studies to favour the theory. Perhaps other historians would regard the doctrinal/ imagistic distinction to be simply irrelevant to the groups they know about, or just plain wrong? To avoid the charge of cherry-picking case studies that fit the theory, we needed a more objective way of testing our hypotheses using a much larger sample. Consequently we decided to build a cross-cultural database on rituals using material extracted from a vast electronic storehouse of ethnographic writings known as the Human Relations Area Files [START_REF] Atkinson | The cultural morphospace of ritual form; Examining modes of religiosity cross-culturally[END_REF].

Over several years we compiled data on 645 rituals from 74 cultures around the world. For each of the rituals in our database we coded for approximately 100 variables, including estimated performance frequencies, indicators of emotional intensity (both euphoric and dysphoric), and a wide range of measures of social cohesion, group organization, scale, complexity, and agricultural intensity. Having such a massive volume of information coded in this way enabled us to look for correlations among our variables of interest -allowing us to test the principal predictions of the modes theory. Fig. 1 shows that the performance frequency of rituals in our database (the blue line in the graph, bottom right) correlated negatively with dysphoric arousal. Moreover there was a clear bunching of rituals around the low-frequency/dysphoric end of the spectrum and the high-frequency/low-arousal end (shown as red cells in the 'heat map' top left). Interestingly, the relationship between frequency and euphoria (the green line in the graph, bottom right -and heat map top right) was more of a U-shaped curve, with the most euphoric rituals peaking around annual frequency.

Establishing an inverse relationship between ritual frequency and dysphoria was extremely important to us because this was one of the central predictions of the modes theory. The initial phase of analysis was a nail-biting exercise because of course our predictions could have proven to be completely wrong -it could quite easily have been the case, for example, that the most painful or frightening rituals occur seasonally or annually. We went on to show that dysphoric rituals are correlated with many of the other diagnostic features of the imagistic mode whereas high-frequency rituals are correlated with those of the doctrinal mode. And we also discovered something else that turned out to be important archaeologically -namely that as rituals become more frequent and less dysphoric, agricultural intensity increases. This pointed to the possibility that the transition from foraging to farming in the early Neolithic may have been linked to the rise of the doctrinal mode. In an effort to find out whether a basic shift from imagistic to doctrinal dynamics did indeed feature in rise of agriculture and the evolution of social complexity, I went to atalhyk in Turkey, the site of an ancient civilization that flourished during the period of transition from hunting and gathering to settled farming, but still long before the first agrarian states emerged. I ended up returning to atalhyk many times, as part of what became a long-term collaboration with the director of the site, Ian Hodder [START_REF] Whitehouse | Modes of Religiosity at atalhyk[END_REF][START_REF] Whitehouse | Modes of religiosity and the evolution of social complexity at atalhyk[END_REF].

atalhyk was a large, densely populated settlement established around nineand-a-half thousand years ago and lasting for nearly two millennia. At atalhyk we have found evidence of a gradual shift from the imagistic practices of small group living to much larger-scale group bonding based on higher-frequency doctrinal rituals. Some of our evidence for this shift comes from pictorial representations of imagistic practices in the early phases of settlement -for example, paintings that show the teasing and baiting of bulls, deer, bear, and boar by crowds of participants and depicting what look like rattles and drums as well as dancing figures. The animals in these paintings would have presented a grave threat to human life and limb. Wild cattle at atalhyk were much larger than their domesticated descendants -adult bulls standing two metres in height at the haunches. Faunal remains at the site show that these animals were slaughtered at large feasting events. We know from the nature of these deposits that feasting rituals occurred sporadically, perhaps once or twice in a generation, consistent with imagistic dynamics. Moreover, the rituals associated with the closure and rebuilding of houses occurred every 70-100 years on average and seemed to elicit strong emotions associated with the handling of human remains.

One of the many clues to the imagistic character of early ritual life at atalhyk is the pervasive emphasis on hiding and revealing, on what Hodder and I have called 'revelatory' practices. Obsidian hoards were placed beneath floors and periodically retrieved. Paintings were repeatedly remade and then covered over. Claws of bears, teeth of foxes and weasels, tusks of wild boars, and beaks of vultures were being placed in walls, repeatedly extracted, replaced, and then covered over. Bucrania were installed in walls and on upright posts. Human skulls and body parts were continually being removed from the floors of domestic dwellings and then reburied and the hiding and revealing of body parts showed remarkably detailed and durable memory for burial locations.

Roughly halfway through the period of settlement at atalhyk the ritual life seemed to change, however. Evidence of the hunting-feasting complex fade and it seems as if imagistic practices are being progressively replaced by a more doctrinally codified belief system, stewarded by orators and expert cosmologists. Hodder and I found many indicators of a more 'discursive' style of religiosity, for example manifested in the designs of stamp seals, paintings, and narrative transmission in what we came to describe as 'history houses'. We saw evidence of increasing standardization of group ideology in the recurring themes of acts and artefacts and especially the way pottery and obsidian production becomes more homogeneous and centralized. We think the emergence of the doctrinal mode at atalhyk constituted a milestone in the evolution of human civilizations, paving the way for more centralized, large scale, and hierarchical patterns of political association. Much previous theory had proposed that this step forward in political evolution was rooted in changing technology and modes of production. But Hodder and I have argued, by contrast, that larger-scale, centralized patterns of social organization were triggered by a shift in people's ritual lives, beginning somewhat earlier than is generally supposed.

One of the ways we have tried to test this hypothesis is by building longitudinal databases. The excavations at atalhyk have so far produced more than 50,000 units of archaeological material, each carefully catalogued in a massive electronic store. We began by recoding selected portions of this material in our own database to include proxies for social complexity, agricultural intensity, and the frequency, scale, and emotionality of ritual performances. This in itself was a major operation and of course raised many challenges of archaeological interpretation. But when we finally came to analyse the data a strong overall picture emerged.

What we found was that rare, emotionally intense, communal rituals decline over the period of settlement and are replaced by more routine family-based rituals. We also saw a shift away from elaborate symbolism and imagery towards more discursive practices associated with mobile objects with standardized designs. These findings were consistent with the view that imagistic practices progressively gave way to more doctrinal ones. We also found a striking correlation between the rise of the doctrinal mode and levels of agricultural intensity. A less welcome discovery, from the perspective of our theoretical framework, was that these processes did not correspond to a growth in the size and density of the population. Actually, we found the reverse -the settlement became smaller and more dispersed. What we think may have happened is that as the doctrinal mode emerged, households became more independent of the communal band and more diffuse ethnic identities encompassing larger and more dispersed populations took over from the local cult group bonded through imagistic practices. Paradoxically, as settlements thinned out and dispersed, social identities may have been greatly expanded -uniting much larger populations.

To test that hypothesis we needed to go beyond atalhyk and examine trends in the changing character of ritual and social morphology over a wider area and over a much longer period of time. For the past three years we have been building a regional database covering nearly all the archaeological sites of Anatolia and the Levant from the end of the Paleolithic to the beginning of the Bronze Age. This has been a labour intensive process involving a larger team but we have now begun to analyse the results. The overall picture that's emerging seems to be consistent with our theory. In the earlier sites surveyed, ritual life bore all the hallmarks of the imagistic mode. Over time large-scale feasting declines and instead a focus on secondary burial becomes a major focus of ritual life, a transformation that we think was linked with the emergence of much larger social groupings based on principles of clanship and descent.

The process of testing the modes theory using ethnographic and archaeological databases has taught us that the history of the mind is simultaneously both local and global and must be studied both qualitatively and quantitatively. One cannot fully understand one aspect in isolation from the other. If, for example, one were to try to understand the relationship between the frequency and emotional intensity of rituals -including their implications for cognitive processes like memory and group alignment -purely by doing qualitative historical research, the picture would be skewed and incomplete. It would be skewed because of the problem of self-selection. And the picture would be incomplete because certain important relationships, for example between ritual frequency and agricultural intensity, are only discoverable through statistical analysis of large samples. But by the same token, none of the quantifiable patterns we are interested in would have been discernible without detailed qualitative data.

Using Seshat to Test the Modes Theory

Seshat will allow us to go far beyond the methods and findings outlined above, to detect a vast range of patterns in the evolution of social complexity, including both short-term and localized trends in the history of the mind -and much longer-term changes across regions and continents. Among other things, Seshat will enable us to test the core predictions of the modes theory longitudinally on a global scale. So let's look at some specific examples of predictions, based on the modes theory, that Seshat will allow us to test, for the first time (for a fuller list, see [START_REF] Whitehouse | The Role of Ritual in the Evolution of Social Complexity: Five predictions and a drum roll[END_REF]).

Our first prediction is that intensification of agriculture correlates with increasing routinization of ritual and the rise of orthopraxy. As we have seen, our surveys of ethnographic and archaeological materials suggest that agriculture leads to more frequent collective rituals. The rationale behind this prediction is that farming requires increasing reliance on regularized forms of cooperationincluding collective rituals. For this prediction, and in fact for all our predictions, we specify an alternative scenario that could, quite plausibly, turn out to be the case. In the case, the alternative might be that routinization and orthopraxy are triggered by factors unrelated to agriculture (e.g. warfare, trade, status inequality, the polarization of economic classes, etc.). So the idea is that we're really using the data to adjudicate between competing possibilities.

Following on from this, our second prediction is that routinized rituals gave rise to larger political units. It's a truism of social science research that rituals bolster the status quo but it is only possible for rituals to serve this function if they enshrine a set of features common to the political unit as a whole.

But in order for a set of beliefs and practices to become standardized across large populations, they need to be enacted with sufficient regularity that everyone remembers their content and meaning in more or less the same way. Repetitive rituals (e.g. conducted on a daily or weekly basis) allow a body of beliefs and practices to become fixed and standardized. We therefore predict the rise of ritual routinization prior to the appearance of large-scale political systems. The alternative of course is that it's the other way around -large-scale polities arose first and routinization came later. Seshat will allow us to find out which is the case.

A third prediction is that the rise of orthopraxy leads to more stable and long-lived civilizations. To the extent that routinization leads to the standardization of beliefs and practices and the emergence of centralized hierarchies, we'd expect the resulting orthopraxy to bolster the status quo and so contribute to the stability and endurance of the social order. Functionalist arguments of this kind once enjoyed almost hegemonic status in the social sciences. But they have been heavily criticised in recent decades mainly on the grounds that not all societies are functionally integrated and stable -and even if some are it is hard to explain how that functional integration came about. Some of these issues will be possible to address systematically for the first time using Seshat. Are certain features of rituals correlated with political stability over time, as we would predict? Are there conditions under which those same ritual features can become dysfunctional (leading to revolutions or failed states, for example)? The modes theory presents a series of sub-hypotheses regarding cyclical patterns of splintering and reformation in doctrinal religions -all of which can in principle be tested using the kind of longitudinal information that Seshat will provide. Now, obviously, the process of testing hypotheses like these, using large historical databases, presents many challenges. The processes involved in converting raw data into something we can analyse statistically are extremely complex. The more complex a methodology becomes, of course, the greater the risk of things going wrong. And there are many other challenges and potential pitfalls to deal with as well. Attempting to calibrate the emotional intensity of rituals, for example, or the extent to which people in the past reflected on the meanings of their rituals, requires a lot of knowledge about historical contexts. Therefore, we need a system for capturing variables in ways that are highly sensitive to the nuances of particular contexts. But how can we be sure that this quality of 'being sensitive to nuances' will be equally achievable for different periods or regions of the world? How do we reduce the effects of ethnocentric bias, for example when constructing items for inclusion in our codebooks? How do we capture degrees of uncertainty about the data -or disagreement among different historians? How do we deal with differences of granularity of data, particularly as we go further back in time?

These problems aren't unique to those of us working with large databases. Actually conventional historical research faces many of the same challenges as a matter of course. In fact, whenever historians make comparisons across periods or regions they run into at least some of these issues. But the task of quantifying history arguably brings these problems into much sharper focus in part because of the scale and remoteness of comparisons. To be sure, comparing some aspect of late medieval and early modern England presents difficulties of cultural commensurability, bias, missing data, interpretive disagreement, and so on. But imagine how much more difficult those issues become when comparing the rituals of, say, Tudor London with those of, say, atalhyk.

We are not alone with these problems, however. In the future, hordes of scholars and scientists will no doubt take up where we will leave off. After 2025, Seshat will become open access. Researchers will be able to test more and more hypotheses about the dynamics of social and cultural evolution in far more detail than has been possible up till now. And there's an even grander hope at the core of this -a hope about harnessing this new kind of knowledge to improve the world around us. Many of us believe that we'll be able to use our new science of history to make societies in the future more humane -maybe even harnessing group alignments in ways that lead to more effective cooperation, and especially more peaceful forms of competition, in centuries and millennia to come.

Fig. 1 .

 1 Fig. 1. Frequency and arousal in a sample of 644 rituals from 74 cultures (reproduced from [1])

Acknowledgements This work was supported by a John Templeton Foundation grant to the Evolution Institute, entitled "Axial-Age Religions and the Z-Curve of Human Egalitarianism," a Tricoastal Foundation grant to the Evolution Institute, entitled "The Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability," an ESRC Large Grant to the University of Oxford, entitled "Ritual, Community, and Conflict" (REF RES-060-25-0085), and a grant from the European Union Horizon 2020 research and innovation programme (grant agreement No 644055 [ALIGNED, www.alignedproject.eu]).


Serge Ter Braake
email: sergeterbraake@gmail.com

Antske Fokkens
email: antske.fokkens@vu.nl

Niels Ockeloen
email: niels.ockeloen@vu.nl

Chantal Van Son
email: c.m.van.son@vu.nl

Digital History: towards new methodologies

Keywords: Digital History, Tool Criticism, Data Driven Research

The field of Digital Humanities is changing the way historians do their research. Historians use tools to query larger data sets and they apply a different methodology to tackle certain research questions. In this paper we will discuss two propositions on the necessity of adapting to and taking advantage of the technological changes: 1) Digital Humanities tools are not the enemy of the historian, but they need to be used in a proper way. This requires historians to make 'tool criticism' part of their methodological toolkit; 2) Digital Humanities tools allow for a more data-driven and bottom-up approach to historical research. This eliminates some of the historian's preconceptions that are inevitably part of more traditional historical research.

Introduction

In 1984 the famous Dutch mathematician Edsger Dijkstra4 wrote his closing speech for a summer school in computer science. He notes that new sciences, like computer science, often are confronted with high expectations, especially if they are not understood very well. 'We all know, how computing is now expected to cure all ills of the world and more, and how, as far as these expectations are concerned, even the sky is no longer accepted as the limit.' 5 More than thirty years later, Dijkstra's observation seems to fit the excitement surrounding Digital Humanities well. The field is still being defined, often misunderstood and while some think it will solve all humanities problems, it is also met with critique and skepticism [START_REF] Liu | The state of the digital humanities. A report and a critique[END_REF] [18] [13, p. 82] [1] [START_REF] Marche | Literature is not Data: Against Digital Humanities[END_REF]. Even though some humanists would argue that the computer is just a tool [13, p. 74], forerunners of the field have always stated that the use of computers is not just increasing the scale of the research, or making life easier for researchers, but it also entails a new way of doing research, a new methodology. The 'Founding Father' of digital humanities, father Robert Busa, stated in 1980 that 'the use of computers in the humanities has as its principal aim the enhancement of the quality, depth and extension of research and not merely the lessening of human effort and time. It has remained relatively unexplored, however, how doing history has changed after the digital turn [20, p. 4] [13, p. 76], or why and how those "new methodologies" are beneficial to humanities research at all [START_REF] Allington | Neoliberal Tools (and Archives): A Political History of Digital Humanities[END_REF]. ' Twelve days before the deadline of this paper Allington et al. wrote a quite hostile essay against digital humanities, which according to them 'was born from disdain and at times outright contempt, not just for humanities scholarship, but for the standards, procedures, and claims of leading literary scholars.' Part of the authors' grumbles seems to stem from their observation that a lot of funding goes to digital humanities research, that is 'promoting methodologies that, until the emergence of these funding sources, had little support within the fields themselves.' [START_REF] Allington | Neoliberal Tools (and Archives): A Political History of Digital Humanities[END_REF] We find this statement surprising because academic progress is by definition achieved through improving old or advocating new methodologies. Investing in something new and promising therefore seems to be a wise thing to do, leaving digital humanists with the task to prove that what they are doing indeed is promising.

As is the case with any new methodology, tool or technique, it should only be applied where and when this makes sense, and in an academically sound manner that respects and takes into account the long tradition of research that has already been done in that field. In this paper we will discuss what is needed to 1) conscientiously and successfully apply digital humanities technology for historical research and 2) apply a new, more data driven methodology to old research questions.

Tools and data

'Algorithms are inherently fascistic, because they give the comforting illusion of an alterity to human affairs.' [START_REF] Marche | Literature is not Data: Against Digital Humanities[END_REF] This quote by Stephen Marche from his influential essay Literature is not data illustrates that humanities researchers are not used to work with algorithms and computational tools in their research. The current section deals with the sometimes complicated relationship between historians and digital humanities tools. Some historians classify these tools either as 'fascistic', or use them without really knowing how they work. Historians cannot be blamed for the latter, since algorithm reading usually is not part of their education (yet). Even some forerunners of the digital humanities field readily admit that they cannot code themselves [START_REF] Allington | Neoliberal Tools (and Archives): A Political History of Digital Humanities[END_REF]. Instead, and rightly so, history students are trained to criticize the sources they work with from the start of their curriculum. The academic value of historical research is doubtful without a sound source criticism addressing questions such as: What is the context in which this source was written? For what purpose? What can be said about the author and their possible beliefs and preconceptions? This holds for both primary sources (diaries, accounts, letters) and secondary sources (history works in which primary sources are analyzed and brought to a synthesis).

The use of digital humanities tools for historical research is complicated for a variety of reasons. Stephen Marche is right in saying that digital humanities tools generally treat texts badly. While historians use texts as the core of their studies, such tools often cut these texts up into 'data' and use complex algorithms to analyze them. This disintegration of text into data is necessary for a computer to be able to do its computations. The potential complexity of the computations often depends again on the format in which the data is stored. It is a valid question however, to ask what the transition from text to data means for humanities research. Marche worries, for example, about the decontextualization of the texts: 'The algorithmic analysis of novels and of newspaper articles is necessarily at the limit of reductivism. The process of turning literature into data removes distinction itself. It removes taste. It removes all the refinement from criticism. It removes the history of the reception of works.'

Besides this 'reductivism' and what it does to the source awareness of the historian, the use of software for historic research, as access portals to the sources, also introduces a new layer between the historian and his/her sources. In 2012 Rieder and Röhle stressed that digital tools rely on assumptions made by humans and should not be treated as being objective. To grasp the finer nuances of a tool however, one may need a bachelor's degree in Computer Science [16, p. 76]. It is not realistic to expect that historians will reach this level any time soon. What we propose instead is a close collaboration between 'tool literate' historians and computer scientists in developing tools for research, which are documented in such a way that historians can understand the basics of how they work and what happens in the process between query and result. It is for example easy to count words with tools like the Google Ngram viewer, but the results should always be interpreted with the way the tool was built in mind, including the estimated reliability, the sources it uses and how it selects elements within these sources [START_REF] Kelly | Visualizing Millions of Words[END_REF]. In other words: historians should get proper training in 'tool criticism', which will allow them to make use of the newest technology in an academically sound manner and to connect traditional source criticism and other historical best practices better. 6 Of course the same applies to academics in other humanities fields, such as media studies [START_REF] Noordegraaf | Computational Research in Media Studies: Methodological Implications[END_REF].

Tool criticism especially comes into play when the computer has to make choices that are difficult to interpret automatically. It is important to realize that even the most logical choices could lead to wrong results. For example: geographical locations in text can be identified using resources such as GeoNames. 7If we would want to know the place of death of a group of people from the Netherlands, then it would be a logical assumption to interpret an ambiguous place name as the one in or nearest to the Netherlands. 8 This could, however, lead to a distortion of the results if we deal with historic place names like Batavia (present day Jakarta). If the software prefers to assign locations that are in or nearest to the Netherlands, it will point to Passau in Germany, or to places in the United States or South America, rather than to the town in Indonesia we are looking for. Automatic disambiguation of geographical locations in text can thus be useful, but the researcher needs to take into account the context in which it is applied.

Another example is related to the sophistication of technology. The OCR quality of digitized texts can lead to misleading results. A part of the Google Books corpus can be queried with an N-gram viewer, which provides the relative frequency of a word over time. Any possible imprecision usually is not detrimental, because of the sheer size of the corpus [11]. In the past, however, the letter 's' in some of the texts with a so-called 'gothic' font would be misread as an 'f'. If a researcher looked, for example, for Amsterdam in the eighteenth century, he/she would get almost no hits. This is not because the town is never mentioned, but because the OCR had transcribed the spelling of the town as 'Amfterdam'. This inaccuracy has been fixed, but it leaves the researcher wondering what else needs fixing and how one can estimate the 'uncertainty' of tools such as the N-gram viewer [START_REF] Traub | Estimating the Impact of OCR Quality on Research Tasks in the Digital Humanities[END_REF].

It should go without saying that the potential usefulness of a tool relies for a big part on the data it runs on. Questions which need to be addressed are: How representative is this corpus to answer my research question? What sources does the tool not use that could (potentially) also provide an answer to my questions? Why was this data used in the first place? These issues are important for traditional research as well, but are especially relevant considering that only a very small part of the archives are digitized [START_REF] Jeurgens | The Scent of the Digital Archive: Dilemmas with Archive Digitisation[END_REF]. It is furthermore important to realize that even if a source is digitized it does not mean it is automatically available for advanced digital text analysis. There is a whole spectrum of forms -e.g. digital photos, OCR-ed text, XML files -in which text can be digitized, which allows digital humanities tools to do a finer or rougher degree of analysis. Hence, historians should be aware of what we might call, the 'granularity' of the data. Certain data sets can be created for a particular purpose with its 'granularity' fit accordingly. Re-using that data set for a different purpose that requires another level of 'granularity' is rarely without complications.

Historians still have to get used to the transition from text to data and the decontextualization that comes with this process. Decontextualization can be partly remedied by a detailed provenance plan and by providing access to the original texts [START_REF] Ockeloen | BiographyNet : Managing Provenance at multiple levels and from different perspectives[END_REF]. This way tools facilitate traditional source criticism. The question remains if this solution also works in practice and, if not, what else could be done to avoid historians drifting away from their source material [20, p. 27]. Historians get access to a wealth of digitized sources, but not in the direct and contextualized way they are used to. Computer scientists (and the historians working with them) need to document their work in such a way that all choices are accounted for. In order to work with digital tools in an academic environment and apply good 'tool criticism', historians need to be able to judge:

-What layers or barriers a tool introduces between them and their sources, and whether these barriers can be overcome; -Whether results produced using the tool are verifiable, either within the tool itself or externally using the provided data; -Whether any assumptions were made in the tool's logical decision making of which understanding must be gained to interpret results; -What possible mistakes could be made by a lack of sufficiently sophisticated technology; -What other data could have been used to answer this question that was not used by this tool; -What the 'granularity' of the data used by the tool is.

Knowing this is one thing, but making sure that researchers and students gain the skills to do all this is another. Humanist scholars already need to have knowledge on a wide variety of fields. Historians, for example, often need to be able to read many (old) languages, need to know know economical, sociological or psychological principles, or need to get acquainted with law, philosophy, statistics and theology. Historians are thus able and used to gain knowledge of other fields, but the question remains if they can and are willing to spend their time learning something new when they already have to know so many things. To persuade them to get some basic tool understanding should therefore start by making them see the benefits of digital humanities research.

It would be more efficient to ensure that history students already have a basic grasp of tool criticism when they leave their universities with a diploma. From our own experience at two Amsterdam universities and from contacts with other European institutions we can tell, however, that humanities students do not eagerly flock to 'Digital Humanities' courses. A recent blog post from Ryan Cordell [START_REF] Cordell | How Not to Teach Digital Humanities[END_REF], in which he also makes some interesting suggestions about how (not) to teach Digital Humanities, depicts a similar situation in Wisconsin: humanities students do not choose history or literature to learn how to program. The nontechnical part of Digital Humanities often is too reflective, too much on a metalevel, for students who are still learning the finer nuances of their field. Instead, Cordell tries to integrate tools and technology into his courses on 'traditional' humanities topics. What we have defined here as 'tool criticism' and the six points which should be judged are quite similar to what is required for proper source criticism. Tool criticism could therefore be relatively easily integrated in the present humanities curricula as a self evident and necessary extension of the traditional source criticism.

Top-Down and Bottom-Up Approaches

The previous section dealt with the sometimes complicated relation between historians and digital tools. In this section we will discuss how digital humanities technology fundamentally changes the way historians approach, or should approach, topics in their field. Without claiming this is the only, or even most important methodological change, we will argue that digital humanities enables historical research to become more data-driven and bottom-up. By taking the available data itself as the starting point for analysis rather than preexisting models and interpretations, some of the historian's preconceptions can be eliminated. We will provide two examples close to our own research of traditional research questions tackled by digital tools. These examples are by no means exhaustive, but will illustrate our point. First we will deal with the theme of canonization of people in history and second with studying of shifting concepts through time.

Canonization of People

Who becomes famous in history and why are interesting questions for historians [START_REF] Smyth | Reputations and national identity, or, what do our heroes say about us?[END_REF]. Traditional historical research would likely start with looking at the currently famous historical people and trace their fame through history by going back in time. This is a labor intensive, but usually fruitful way of approaching such a topic. Many female heroines, whose fame had been debunked in the nineteenth century of chauvinistic academic history writing, are being restored this way [START_REF] Kloek | Kenau en Magdalena[END_REF]). Until recently however, it was nearly impossible to trace fame for larger groups of people. The Google Books team tried to quantify fame between 1800-2000 by searching for names in the corpus of Google Books [11]. One of the authors of that paper, Adrian Veres, also created a science Hall of Fame, in which he ranks the scientists in milli-darwins, based on the number of mentions in the Google Books texts. 9 Ironically, these computer scientists still used a classic historical approach to study fame. They used existing lists of famous people and mapped their fame through time, running the risk of missing out on people that were famous in their own time but did not make it to the modern canon (and also missing possible instances of 'Charlef Darwin').

Digital Humanities tools make it possible to approach the topic differently. In recent research, we proposed a methodology to get around this top-down bias by extracting any names from digitized text and disambiguating them. We tracked the names by searching for strings of capitalized words (like 'Johan Cruijff', but also 'Johan de Witt' and 'Joan Derk van der Capellen tot den Poll'). Even though this results in lists of names with a lot of noise, it is easy to manually filter out strings that do not refer to people (like 'Den Haag'). We disambiguated the names mainly by using time stamps to separate people with the same name. When running tests on the data of the Biography Portal of the Netherlands, we traced a preference for the house of Orange, foreign rulers and Jesus Christ as being the most frequently mentioned individuals in other people's biographies [START_REF] Ter Braake | How to Make it in History. Working Towards a Methodology of Canon Research with Digital Methods[END_REF]. The availability of more machine-readable texts and tools to analyze them therefore makes a completely data-driven and bottom-up approach to the study of canonization in history possible. By approaching the topic bottom-up the narrow selection of predefined canons can be avoided, for as far as the digitized material allows this.

Tracing Concepts through Time

'Concepts' and how they change over time have a decade-long history of being studied by historians. A concept is a notion or an idea, referred to by one or several words, and which has certain attributes that can change over time. Especially German historians spent a lot of time and resources to get a grasp on concepts. In the Geistliche Grundbegriffe project a German team of scholars studied state formation and the course of history in general between 1750-1850 for decades, by seeing how certain concepts, or words related to concepts, changed meaning [START_REF] Richter | The History of Political and Social Concepts. A critical introduction[END_REF]. In sociology some speak of contested and contestable concepts, like 'democracy' and 'freedom', on which people never seem to agree [START_REF] Collier | Essentially Contested Concepts[END_REF]. Contested concepts are considered to be of great interest to study in order to see how the political climate changes in a certain period of time.

One thing that these studies have in common is that the researchers determine in advance what concepts are worth investigating, and that they look into how these concepts fit into an already conceived model. Of course historians have already read a significant number of texts on which they base their ideas and hypotheses, before studying anything more closely. Still, their approach is mainly top-down. Once a certain topic is singled out, it usually has to be brought to completion, simply because it is too labor intensive to easily abandon. Similar to the research on the canonization of people in history, one runs the risk of interesting concepts not being found because of this necessarily subjective approach to the selection of a topic.

Fortunately, concepts are a fruitful topic for digital humanities research, as was showcased recently on a workshop in Helsinki. 10 When the necessary digital material is available, a few simple, exploratory exercises could already help determining the most sensible direction to answer a research question. The concept of 'nation' for example was still very much in flux in the nineteenth century, and possibly referred to by many different words. It is difficult to determine in advance then, what terms are worth investigating. For our research we have text files available of all published volumes of the Dutch liberal and intellectual monthly journal De Gids, in which nation ('natie') is a frequent topic. We can search for terms like 'natie', read the key texts and determine what related terms are worth investigating. An alternative and possibly a more neutral approach is to count all words in a certain period of time and look closely at what is discussed and how this is related. In a corpus of 396,963 words from the first year of De Gids in 1837, we find only 20 instances of the word 'natie', but 114 of 'vaderland' ('fatherland') and 106 of 'volk' ('people'). Tracing the shifts in those terms and similar words will be a good starting point for further investigations.

One of the most neutral more sophisticated approaches is that of Tom Kenter et al. [START_REF] Kenter | Ad Hoc Monitoring of Vocabulary Shifts over Time[END_REF], who trace concepts through time by starting with one or two 'seed words' and use vector coordinates to see what terms are associated with it over time. Words that occur frequently in the same sentence together get vector coordinates close to each other and are likely to be connected somehow. In our case above regarding the concept of 'nation', it would be interesting to determine if frequently occurring words like king ('koning), foreigner ('vreemdeling'), language ('taal'), freedom ('vrijheid') and religion ('godsdienst') are closely related or if they mostly occur in different contexts. The chosen seed words will still be predefined, but they can be based on the findings of simple preparatory text analysis. This methodology also allows for a more flexible way to approach research on concepts and may result in leads that would otherwise remain hidden. By tracing the concepts underlying these seed words for a longer period of time, it is eventually even possible that the original seed words will disappear. To use the example of Kenter et al.: the concept of portable music player may be behind the seed word I-Pod, but course 'I-Pod' will not be present in texts from the twentieth century.

Conclusions

Historians and other humanists can be skeptical about technology and ask what kind of true innovations digital humanities technology brings to the field of history. Part of this skepticism has to do with a too narrow focus on the tools and how they work and too little on the way they alter existing research methods. This paper provided some reflections on how the field of history can, or maybe should, adapt to the changes brought by the field of digital humanities and can progress thanks to it.

Tools will become increasingly more reliable and interesting to use. Historians should be aware of the barriers a tool erects between them and the sources, whether the results are verifiable, what sources could and could not be used, what biases the tool has built-in and what its technical limitations are. Such 'tool criticism' is necessary to see what tools do with historical sources, to determine what this means for the interpretation of the output, and to see how this can lead to innovations on how history should be studied. This criticism could also be an essential part of a criticism-improvement feedback loop between historians and tool creators.

In our view one of the most exciting new opportunities digital humanities tools bring is a rigid studying of history bottom-up. Computers can read data far quicker than humans can, which allows for a fast and more extensive exploration of the sources before determining who or what to study more in depth. Instead of applying a ready-made model to one's sources, current technologies increase the possibility of letting the data 'speak' for itself, experiment and try to find the interesting leads in it, which may eventually lead to new models.

https://en.wikipedia.org/wiki/Edsger W. Dijkstra

https://www.cs.utexas.edu/users/EWD/ewd08xx/EWD896.PDF

In May 2015 a small workshop on tool criticism was organized in Amsterdam

http://www.geonames.org/. Though of course we have to be cautious here, since it is difficult to 'translate' historical place names to modern names, and problematic because the geographical region covered by a town will be smaller or bigger throughout history.

[START_REF] Kloek | Kenau en Magdalena[END_REF] A similar example can be found in Ockeloen et al.[START_REF] Ockeloen | BiographyNet : Managing Provenance at multiple levels and from different perspectives[END_REF]

http://www.sciencemag.org/site/feature/misc/webfeat/gonzoscientist/episode14/index.xhtml

http://www.helsinki.fi/collegium/events/conceptual change/index.html

Acknowledgements

This work was supported by the Amsterdam Academic Alliance Data Science (AAA-DS) Program Award to the UvA and VU Universities and by the Biog-raphyNet project http://www.biographynet.nl (Nr. 660.011.308), funded by the Netherlands eScience Center (http://esciencecenter.nl/).