Karin Knorr Cetina
email: knorr@uchicago.edu

What If the Screens Went Black? The Coming of Software Agents

Trading screens are not supposed to be black. In fact, when we see them on trading floors, on TV, or in media centres, they attract us with catching colours and blinking information. They project urgency, speed, and power -the power of big money, the power of winning and losing. When we are near them, we feel their heat.

We want to give in to their considerable attraction. We want to be players of the game and part of the action.

Of course, the screens are only a medium. What beckons us is the market. All the beguiling signals come from a market that moves -prices, volumes, ratios, differentials. They move in a context and in response to it. The context, of course, also moves, but usually more slowly. How often do politicians make decisions? How often do tsunamis strike? The context is everything that happens in the world that may have an impact on the market. But that everything happens on a human schedule and is adapted to how we conduct our lives and how nature and the environment conduct theirs. What parts of the context impact a market? The question is ongoing and difficult.

Should the stars and their movements have a presence onscreen? Astrologists may think so, but Reuters and Bloomberg don't, and they are two of the largest companies feeding material to the screens. Is Namibia a country that should be present in a global financial market? It has its weekend and peasant markets, to be sure, and some stocks, but its gross domestic product is shrinking and is little more than a rounding error on the trillion dollar economies that count. Trading screens cover the three major time zones and not every little nation and group that populates the globe.

Even with these restrictions, however, trading screens are rich in texture and information. They are not just surfaces. Behind every window, dozens of others can be opened and closed. Calculations can be called up and made on the spot. Information can be requested and is instantly available, at all times. Social relations carry on and jokes are made. And, of course, one trades through the screen, through the electronic channel. All trading in currency markets, for instance, is conducted electronically.

Screens are rich, and they are not only there for trading. They are the world for those who sit in front of them. The question posed at the beginning of this essay is larger than it may have initially seemed. When the screens go black, a world goes black. But what happens if that world goes black? Twenty years from now, even in ten years, the answer could be "nothing at all". The screens project the market, but in the future the market might carry on just as well without screens. The contextual information will be necessary and will remain available -available to programs that receive and decode it without displaying it onscreen. The scopic regime under which electronic

financial markets operate could simply migrate fully underground. Why "scopic" and why "regime"? Financial screens are like scopes. A scope is an instrument for observation. A periscope, for example, protruding from the surface of the ocean, can give a submarine a 360-degree field of view -a view richer and deeper than that of the naked eye. A telescope, using lenses or mirrors, can "see" the objects that circle us on planet Earth -it brings close what's behind the twinkle in the sky that the eye can see. The screens that project the market are not optical instruments, of course. They bring the activities of globally dispersed participants near and condense them onscreen, making them visually available to everyone connected to the information technologies that produce and transmit the screens' content -in real time and in real speed. News about changes in the market's context augment financial content: political activities (e.g. elections), announcements related to the economy (e.g. Fed forecasts and earnings calls), social events (e.g. riots), cultural trends (e.g. tourism), and natural disasters (e.g. floods). The observational system at the centre of a fully electronic financial market then uses its knowledge of market transactions and external events to perform instant analyses of the material. It combines new data with historical content and enriches both with financial indicators processed elsewhere and released to the screen. This extension in the scope of financial transactions to, in principle, all that is globally pertinent to the market, as well as to the analysis of these materials, is part of the notion of "scopic". A scopic regime then simply refers to a combination of technological media, software, and information that enables a global financial market. I call it a regime because scopic media rule the market, so to speak. Without these media, the market could not operate. Of course, the actors in the market could try to go back to telephone trading, or even to voice trading as it once existed in alleys and streets.

But unless all computerized infrastructures break down, no one in the foreign exchange market, with its $4 trillion daily turnover, could survive. A scopic regime forces actors to participate in the current venue if they want to be in the market -though it also allows and even encourages improvements, customization, and changes of venue. The point of the current venue is that it works by making the flow of the market visually available in augmented fashion for human actors to perceive, interpret, and act upon. It also streams the market, putting it in a visually running sequence. In other words, it continually updates the market as new information flows in and old information flows out (or scrolls down the screen).

When things migrate underground, something splits off the huddles of traders and screens of the trading floors we know. When it splits off and turns inside, so to speak, another world opens up underneath the level of human agency: that of data centres, programs, and electronic connections. This is the environment of algorithms, or as we may want to think of it, of software agents. Software agents operate underground already, but they only partly substitute for humans.

Human traders are still on the floors and in exchanges, watching the market, and watching what their "tools", the algorithms, do. Traders refer to algorithms as their tools. Having a tool suggests that you have control over it and that it serves you. In the foreign exchange market, for example, as judged by evidence I have from two top banks in worldwide trading volume, trades valued at over $5 million (first bank) or over $50 million (second bank) are executed by human traders.

Those valued less than these amounts are left to algorithms, but human traders oversee the activity.

The British Foresight report that investigated the future of computer trading just recently stated that: "Despite the increase in computer power, processing speed and sophistication of computer algorithms, the present day financial markets still involve large numbers of human traders". And it continues: "There are good reasons to expect, for the next decade or so, the number of human participants in the market will remain significant" [START_REF]Foresight: The Future of Computer Trading in Financial Markets[END_REF] (p. 32).

As the timeline and the way things are hedged in this formulation suggest, the presence of algorithms already is quite significant. Today, traders expect that when things migrate underground fully, perhaps after the next decade, some human traders and others are likely to remain on the floor and continue their work. For current traders, it is hard to imagine a market without human participants, or without screens. Such an arrangement would not be possible, they tell me, since someone has to be there to watch and think and to develop more tools. Let's assume they are right and ask: what it would mean for our trading culture and sociology if the screens went "half black"? What would it mean if traders were still around but had limited roles to play, and underground agents had taken over plenty of trading functions? I want to turn to this shortly, but before I do so, here are some data and definitions, and an explanation of algorithmic trading.

Electronic-Automatic-Algorithmic: Some Data and Definitions

The Bank for International Settlement (BIS) described the rapid growth the foreign exchange market experienced between 2007 and 2010, when it was last measured, as the "the $4 trillion question: what explains FX growth since the 2007 survey?" [START_REF] King | The $4 Trillion Question: What Explains FX Growth Since the 2007 Survey?[END_REF]. The foreign exchange market had grown during that time, the time of the financial crisis (!), from a daily turnover of $3.21 trillion to approximately $4 trillion -after an already unprecedented rise in activity by 69% between 2004 ($1.420 trillion) and 2007. The $4 trillion question can be answered, the BIS says, by a 20% surge in high-speed buying and selling by computer programs using algorithms.

The number of nonfinancial institutions, or retail traders, also increased during the period, making up almost 10% of the global market in 2010 [START_REF] King | The $4 Trillion Question: What Explains FX Growth Since the 2007 Survey?[END_REF]. These non-banks and day traders increasingly use algorithms offered to them through the trading platforms they employ. Several sources, including the BIS, estimated the total volume of automated trading based on algorithms in the foreign exchange market in 2010 to be about 25% [START_REF]High-Frequency Trading in the Foreign Exchange Market[END_REF] (p. 11). Traders interviewed in 2013 thought it had remained roughly around 30%. The volume varies between banks, as some banks make a stronger push towards automated trading using algorithms than others. Barclays, for example, was said to have only four spot traders left in foreign exchange, and they manage trades worth over $50 million -everything else is done automatically through the "Algobook". One of the two largest Swiss banks joined the push towards algorithmic trading, and the other appeared to still ponder it in 2012 (interview data). As the BIS report noted, "FX dealing banks unsurprisingly do not welcome the compression of spreads" associated with the rise of high frequency trading, one of the main trading strategies using algorithms [START_REF]High-Frequency Trading in the Foreign Exchange Market[END_REF] (p. 9). 1 The total number of this sort of automated trading is considerably higher in the stock market. The Foresight Report estimated that 1 "Spreads" here refer to the difference between bid and ask or buy and sell prices from which traders profit. the percentage of trades that are automated can be as high as 60% in US equity trading, with estimates ranging from 30% to 60% for European equity markets. Equity markets are not globalequities can be purchased or sold globally, but they are organized regionally in exchanges -hence the differences between Europe and the United States [START_REF]Foresight: The Future of Computer Trading in Financial Markets[END_REF] (p. 43).

Automated trading is electronic trading that depends on one or more algorithms at some stage of the process [START_REF]High-Frequency Trading in the Foreign Exchange Market[END_REF] (pp. [START_REF]High-Frequency Trading in the Foreign Exchange Market[END_REF][START_REF] Durbin | All About High Frequency Trading[END_REF]. Electronic trading that is not automated uses computers to trade, exchange information, chat, and everything else scopic media allow one to do through a screen.

Electronic trading is, then, manual and "high touch": a trader hits a key to trade what's on offer on an electronic broker system, which displays the bid and ask prices and volumes onscreen, or a trader sends a message to deal directly with a counterparty using an onscreen conversational dealing system. Electronic trading also can be "medium" or "low" (human) touch, and only then is it algorithmic (and electronic) trading; more or less, the work of human traders -scanning prices and executing trades -is performed by algo-tools. When does it become what one might call "algo" touch? There are various types of trading based on algorithms; for example, they include the vast area of algorithmic execution, in which the computer program is responsible for executing a trading order. This sort of algorithmic trading "is the bullet, not the finger on the trigger", since the algorithm isn't making trading decisions [START_REF] Johnson | Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies[END_REF] (pp. [START_REF] Durbin | All About High Frequency Trading[END_REF][START_REF] Mullins | Traders Pay for an Early Peek at Key Data[END_REF][START_REF] Popper | High Speed Trading No Longer Hurtling Forward[END_REF][START_REF] Perlberg | Now You Can Do Algorithmic Trading From Your Couch[END_REF][START_REF] Cendrowski | Quant Trading Comes to Main Street[END_REF][START_REF] Schneider | An Invitation to Computer Science[END_REF][START_REF] Aldridge | High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems[END_REF]. But algorithms that make trading decisions are increasingly common, and are based on models and incoming information. For example, they can read the news and interpret current events, and in response they can execute a trade and hedge against it. When the algorithm has its finger on the trigger, we can extend the current parlance and describe it as "algo" touch. Humans still outperform algorithms in analysing the semantic information carried in human-readable data streams, which range from written stories to audio and video sequences and tweets on social media websites. But news analysis performed by algorithm is a significant focus of research, with the goal of enabling computers not only to understand the numerical information of market prices, but also to understand non-numerical information. As this research advances, we will approach the "algo" touch mode, in which no human trader will be involved in trades, but in which software agents will be essential. Of course, the human touch in this case will just shift to other professions: for instance, to engineers and programmers that develop the software agents.

Is a software agent a professional? An algorithm is simply a set of instructions for accomplishing a task, according to various definitions. It may be a formula, or a set of rules, a set of steps, or on the most general level an approach to solving a problem. The latter definition sees an algorithm more as an idea behind a set of instructions. There are likely to be many possible solutions to any problem, and for that reason there can be many different algorithms trying to accomplish the same thing. Even when the problem seems as simple as finding an entry in a phone book, there are different ways, as Durbin explains, of solving it: we can start from the beginning and flip pages until we find the name, or we can guess where in the book names with the first letter of the entry might appear and flip back and forth till we find it, or we could possibly work with an index, if one exists. 2 On the programming level, an algorithm is a computer code. When you write a program to filter the light in a certain way so as to create a specific photographic effect, you have created an algorithm. The use of algorithms in trading is not new. The Electronic Broker System (EBS), developed by a consortium of banks in the early 1990s, is a combination of programs that on the most basic level helps prospective buyers and sellers find one another. It does this by sorting incoming trading requests according to the best bid and ask prices, putting them in a sequence, by distinguishing and adding up volumes, by supplying the next price once a price-volume offer has been consumed, and so on. To do this, it needs criteria and information and the capability to sort, compare, archive, etc. An order-executing algorithm, for example, may simply perform order slicing; that is, it may split a large order into smaller segments of "child orders" that are put on the market (or sent to the exchange) sequentially, perhaps every hour. The goal is to reduce the impact one large order can have on the market price of a given security. A human trader likely would initiate this order by placing it in a more sophisticated way, for example, by placing individual child orders on the market at irregular times. A trader might also consider price movements before executing a portion of the order, and so on. Algorithms evolved at least in part by becoming more sophisticated by imitating experienced traders. For example, they learned to include more randomization -to elude market participants able to identify the linear actions of a tool that trades according to a fixed schedule. Then they became responsive -they learned to execute orders in response to live market volume -instead of using historical volumes to determine when to execute a child order. And then, according to Johnson, they started to base their responses on particular types of analysis or conditions, evolving within categories of algorithms for a while, before a new generation of algorithms took hold [START_REF] Johnson | Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies[END_REF] (p. 14). The first generation of algorithms learned from humans -it is the one that evolved from a linear fixed execution schedule to responding to market 2 I take the example from [START_REF] Durbin | All About High Frequency Trading[END_REF]. conditions in order to reduce market impact. The second generation learned from theories and models as it became more price and cost sensitive -it used transaction cost analysis to estimate and reduce transaction costs. That is when algorithms began to move beyond trying to imitate the behaviour of experienced traders. The third generation became reflexive -algorithms learned to examine and use data and venues that the market itself provided. For example, they learned to examine order books, which became more widely available as markets transitioned to electronic trading platforms, and to use more than one execution system, which also became available -e.g. electronic crossing networks (ECNs) and alternative trading systems (ATSs). This generational mobility will continue naturally, one assumes, though it may also be pushed forward by systeminternal dynamics. Algorithms can be copied, which can eliminate their advantage. As a consequence, one looks to identify new advantages. The most sought after of these in the last ten years has been time advantage, which is used by high-frequency trading (HFT).

HFT is another subset of automated trading. It uses the famous processing power of computers to monitor and analyse thousands of variables to optimize trades. The latency periods exchanges achieved in 2010 were below 10 milliseconds -a real blink of an eye takes 300 to 400 milliseconds. HFT's first trademark characteristic is the use of extraordinary high speed together with sophisticated algorithms, a mix that is hardly beatable by a human being. IBM's Deep Blue began to overtake human chess masters in the late 1990s; 3 HFT trading took off only around the middle of the 2000s and reached volumes accounting for more than half of stock trading in the United States in 2010. There are signs that this expansion is ending, given an increase in competition, the high costs of HFT, and new regulatory restrictions. HFT firms may co-locate their computers at exchanges to improve the speed of computer linkages, and they use data feeds offered for purchase to gain processing advantages over others in the HFT universe. One example that recently has caught the attention of US regulators is the sale of early financial data by exchanges and information provider firms like Thompson Reuters and Bloomberg. Thompson Reuters, for example, offered the release of the University of Michigan's consumer confidence index, a marketmoving economic indicator, two seconds early to paying clients, and the company in turn paid the university "at least $1million a year" to distribute the data early to its customers [START_REF] Mullins | Traders Pay for an Early Peek at Key Data[END_REF]. NASDAQ and the Chicago Mercantile Exchange promise to deliver NASDAQ data to Chicago customers two milliseconds faster than they are otherwise available using microwave transmission, at a cost of $20,000 per month to subscribers. Such costs of the race to receive information first, together with the costs of shaving additional milliseconds off trade times through the co-location of servers with exchanges and through building new data connections (e.g. from Chicago to New York), make it harder to generate profits, particularly during market downturns. Traditional investors, like mutual funds, have embraced the high-speed industry's automated strategies and moved some of their business away from exchanges that are popular with high-speed traders. "Profits from high-speed trading in American stocks", the New York Times reported in October 2012, "are on track to be . . . down 35 percent from last year and 74 percent lower than the peak of about $4.9 billion in 2009", citing the estimates of a brokerage firm [START_REF] Popper | High Speed Trading No Longer Hurtling Forward[END_REF]. "Wells Fargo and JPMorgan Chase each earned more in the last quarter than the high-speed trading industry will earn this year", it added. HFT firms also were accounting for "a declining percentage of a shrinking pool of stock trading, from 61 percent three years ago to 51 percent now", according to the Tabb Group, a data firm. The report also mentioned that firms have been cutting staff, or even have shut down [START_REF] Popper | High Speed Trading No Longer Hurtling Forward[END_REF].

HFT has several other features having to do with the short time horizon of trading. For example, traders hold positions only for very short periods of time, they may submit large numbers of orders only to cancel them soon thereafter, and they end the trading day "flat", without carrying significant positions (inventories of an investment instrument) overnight. 4 Observers also note that HFT firms implement typical trading strategies such as classical arbitrage, which exploits the difference between actual market prices and prices implied by "no arbitrage" conditions; latency arbitrage, which exploits the short time lag between the moment a trade occurs, the moment an HFT player detects the resulting change in the security's price, and the moment an update is made to the price quoted by market making traders; 5 liquidity redistributing strategies, which detect imbalances in order books and pricing discrepancies across trading platforms; and "complex event processing", which exploits properties of currency pairs (e.g. correlations among pairs of currencies and other assets) to find profit opportunities. There are also -to return now to the more general level of automated trading -different styles of investing by which one can classify the use of trading with algorithms. If HFT is one style, statistical arbitrage is another. This is a term used for trading that spans longer time frames than HFT, but statistical arbitrage seeks to exploit mis-pricings between correlated assets in a similar way. Quantitative trading based on proprietary models whose actual mechanisms are not disclosed is a third style. Some authors, like Johnson, reserve the term algorithmic trading for the execution of orders [START_REF] Johnson | Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies[END_REF] (pp. [START_REF]High-Frequency Trading in the Foreign Exchange Market[END_REF][START_REF] Johnson | Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies[END_REF][START_REF] Durbin | All About High Frequency Trading[END_REF].

Transformations

What little I have said about types of trading with algorithms and some of the issues this raises may at least suggest one thing -if the screens go black, or more realistically, if they go just half black, and the world that emerged with digitalization since the 1980s fades into history, then another world will rise in its place, one full of actors, business models, attempts at advantage taking, and sophisticated trading strategies. And while there are important differences between the former and the latter, markets will not end, and in some cases, as in the case of the Foreign Exchange Market, they may stealthily become larger and richer rather than smaller. The new world will be curated and monitored by humans. What's the difference between a market curator and a trader? Or more broadly speaking, what is the sociology of the new world with its understructure of software agents that work, make decisions, and determine investment success?

And what happens to what I called the scopic regime when algorithmic traders become widely used? The first answer is that algorithmic traders and their effects have to be monitored and observed. Algorithms move underground, or rather, into the belly of computer networks, where computer programs do their work. They are software agents that traders don't get to know face to face on trading floors or during happy hour at a bar. What they do will be known through the textual descriptions created for them, but in the market we will know them mainly through their effects.

Traders may not trade anymore below a certain volume -employing their algo-tools for that purpose -but they surely will continue to monitor the market, the price and volume movements that result from human and algorithmic trading, and they will continue to monitor outcomes of software agents' activities. Automated trading systems will follow their logic regardless of the outcome, as Mary Schapiro, then chair of the Securities and Exchange Commission, told a congressional committee in the wake of the flash crash of 2010. 6 Human monitoring is needed to prevent this from happening, in addition to the circuit breakers that have been implemented in 6 As reported in [START_REF] Salmon | Algorithms Take Control of Wall Street[END_REF]. See also [START_REF]) Commodity Futures Trading Commission: Findings Regarding the Market Events of May 6[END_REF]. exchanges to automatically halt trading when prices of stocks fluctuate by more than a certain percentage.

These may appear to be minor changes in trading practice, but they reflect a tectonic shift on a macro level that is less benevolent for those they affect. The most drastic change we witness today occurs on a professional level, and its most obvious effect is the historical replacement of one elite profession by another, in which the losers are human touch traders, and in exchanges, where the losers are voice brokers. The winners for now are professionals with a higher education and one or more degrees in the relevant disciplines: software engineering, mathematics, statistics, physics (physicists tend to be trained in both and know some informatics), and finance. The institutional winners are speed-focused trading firms, hedge funds, or proprietary desks and funds internal to banks, in which some of the new professionals work. To some degree, and in some areas, the displacement involved a power struggle; traditionally powerful and dominant exchanges, for example the New York Stock Exchange (NYSE), were slow to adapt to the new realities, and in the process lost volume and status. The NYSE, whose roots go back more than two hundred years, once held a preeminent market position at the apex of the world financial system, but it now accounts for only 22.5% of all stock trading. The replaced category of actors subsequently has moved into asset management, private trading groups and firms, or has been retrained. For example, there has been an exodus in recent years of traders from (investment) banks to proprietary trading firms and hedge funds, which have greatly increased in number. "Hundreds of talented traders" have left as banks shrank their trading operations in anticipation of the regulatory overhaul, the Volcker Plan, which would bar banks from such proprietary trading. "They are armed with skill sets", this report continues, "that are in demand at hedge funds. Some are launching their own funds -a total of 549 new hedge funds were launched during the first six months of this year (2012) . . . while others are joining established ones . . . Rather than wait for banks to close their 'prop desks,' many star traders have gone into business for themselves over the last couple of years" [START_REF] Shari | The Volcker Generation[END_REF]. The demand illustrated here may help explain the lack of a power struggle in some areas, but this is only part of the story. Many former traders, including those with technical and organizational skills, Currency strategy experience is strongly preferred. 7 What about the non-human participants, our algorithmic agents? Their preferred qualifications are less mixed, as algorithms tend to be specialized. What they can do technically within their specialized functions already has been illustrated in the previous section. But how should these functions be executed, or, in other words, what properties do we look for in a trading algorithm?

We won't find job descriptions for software agents on the web, but, luckily for us, computer science itself talks about desirable properties of algorithms and how these properties should be determined.

Developing a solution to some problem typically involves at least four steps: "(1) designing an algorithm or step-by-step procedure for solving the problem, (2) analyzing the correctness and efficiency of the procedure, (3) implementing the procedure in some programming language, and (4) testing the implementation". 8 Analysing the qualification of an algorithm in terms of its efficiency in the present context means, first and foremost, measuring its speed in executing tasks.

Speed depends on several factors, for example:

• the size of the input ("searching through a list of length 1,000 takes longer than searching through a list of length 10")

• the algorithm type ("Unordered-Linear-Search is inherently slower than Binary-Search")

• the programming language used ("interpreted languages such as Basic are typically slower than compiled languages such as C++") 7 See www.simplyhired.com/job/foreign-exchange-trading-quantitative-analyst-ph-job/greenwich-based-hedge-fundfinance-industry/z6bw2hxuqg (accessed 12 May 2014). 8 For this and the following list and its explanation, see [START_REF] Aslam | Analysis of Algorithms: Running Time[END_REF].

• the quality of the implementation ("good, tight code can be much faster than poor, sloppy code")

• the speed of the computer executing the code.

In analysing the efficiency of an algorithm, Aslam and Fell [START_REF] Aslam | Analysis of Algorithms: Running Time[END_REF] continue, one typically focuses on the speed of the algorithm as a function of the size of the input on which it is run, and one determines the number of program steps or some count of other computer operations as a function of the input size -the actual time, however, still also depends on the programming language used, the quality of the code produced, and the gigahertz (the speed) of the computer. In other words, an algorithm needs to live up to very particular and measurable criteria. Speed is just one of these criteria, though its importance in trading cannot be overestimated. Other criteria include precision (the more accurate the solution is, the better), memory (the less required, the better), optimization (some instructions, when repeated, may lead to learning and greater success in meeting a particular condition), and so on. If this sounds different from how we "size up" humans, even when they compete over speed, when they run a race for instance, then that's because it is. Algorithms are not expected to have the same properties that humans do. Although they started out more or less imitating human behaviour in trading and are still advertised as being capable of doing exactly that (see [START_REF] Perlberg | Now You Can Do Algorithmic Trading From Your Couch[END_REF][START_REF] Cendrowski | Quant Trading Comes to Main Street[END_REF]) algorithms do think differently from "us". "An algorithm is a well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time" [START_REF] Schneider | An Invitation to Computer Science[END_REF] (p. 9) -I know of no account according to which a human being would fit this description.

Let me summarize, then, some of the differences between "us" and the algorithms that traders confront in the market, before ending with a brief outlook. The qualities of "them", in contrast to those belonging to "us", are taken from what humans dealing with algorithms attribute to them. 9 Table 1 offers an overview. 9 These include my own interview sources and writings on algorithms by insiders in algorithmic trading. See for example [START_REF] Aldridge | High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems[END_REF]. The first difference between humans and algorithms is that humans have emotions and that algorithms don't. Neurophysiologists think emotions are needed for intelligent human behaviour, but traders, taking their cue from the trading lore and culture, often maintain that emotions must be curbed, and that they can be controlled by "discipline", a second characteristic and skill humans have. 10 Trading culture recommends that traders be "disciplined". A trader should take a view of how a stock or currency or other financial instrument will do within the relevant time period, and stick with it. The trader should liquidate a position when a price exceeds a pre-set limit, or when it gets too low, rather than wait and see if the investment will recover. One of the relevant stories that circulates among investors is about a famous trader who would leave the trading floor and go to the movies whenever markets were in turmoil -in order to maintain discipline and not react in impromptu ways. Discipline is recommended and taught to novices working on the floor to rein in their emotional responses. Algorithms, of course, don't need to have discipline, because they are disciplined by nature -programmed to do certain things and not others, and programmed to follow rules.

A third difference is that human beings are communicative actors using natural language rather than formalisms. Natural language words mostly have no fixed, unambiguous meaning.

Meanings are pragmatic. They depend on context and the way words are used. Think of the fact that most dictionary entries have more than one listed meaning, and then consider the various ways 10 For an example of a neurophysiologist who thinks emotions are needed for intelligent human behaviour, see [START_REF] Damasio | Descartes' Error: Emotion, Reason, and the Human Brain[END_REF].

individual words can be used towards different ends. Indeed, users may give a word nearly any meaning they want when they speak metaphorically or poetically. Natural language utterances and writings require interpretation, a skill humans learn by talking, textual exegesis, and training. For example, humans grow up "taking the role of the other" in Mead's famous phrase. They learn by putting themselves in others' positions and seeing the world (and themselves) from other points of view. That helps with decoding and understanding others' intentions. Humans effortlessly and without explicit awareness pay attention to gestures, reading them as additional information. And they use all of their senses and not just their vision or prefrontal cortex to "understand" what they see. As they grow into a particular language and culture, they acquire a huge repertoire of implicit knowledge that they can bring to bear on familiar and unfamiliar situations. Based on their understanding and experience, human traders, for instance, usually have control over their trading decisions, using their own judgement. They can make discretionary judgements. They are not bound by the data and calculations they receive from analysts and sources on their screens, and they usually are not bound by the view their employer, say a global bank, has of the market.

Algorithms don't make discretionary judgements, and they are programmed to respond to data.

Things get murkier, however, when we ask whether algorithms have interpretive skills. The answer is that algorithms currently are learning to read and decode texts in order to interpret incoming news items correctly, but, as of today, they are still making substantial errors, for example, by taking messages literally and without taking into account circumstances and contextual clues. There are more differences between algorithms and humans, as the trading side sees it, and one is that while we consider ourselves intelligent, and undoubtedly are, algorithms are intelligent and simultaneously dumb. They are capable of making informed decisions, but they will make them "scientifically", strictly on the basis of data they receive and the analysis they conduct or that other algorithms conduct for them. The downside of this is that they cannot think "outside the box", outside what they are programmed and optimized to do. Algorithms can learn, for example, by going through iterative cycles of runs, within a given framework, but they appear to be less adept at responding circumspectly and cleverly to new situations. They can also be imitated and "outprogrammed" once a party interested in doing so understands what they are designed to do.

Humans, of course, have major drawbacks, too. One is their limited speed, the dimension along which algorithms always will outperform human beings. Their speed allows algorithms to conduct research practically instantly, for instance, while human analysts or traders need a substantially longer period of time for similar results. Both humans and algorithms tend to specialize in certain tasks and enjoy a division of labour, though algorithms surely always were more specialized than humans. This difference may become more pronounced in the future, as the ad cited above suggests.

One of the seductive features of algorithmic trading is, of course, that once an algorithm has been developed its continued operation costs less than the labour of a human trader. This is a major driving force for algorithmic trading.

Our Postsocial Future

Algorithms think and act quite differently from us, as we on the human side see it. Notions such as "thinking", "intelligence", and others mentioned previously in this essay, such as "seeing", "reading", and "acting", suggest an agent. Are algorithms "tools", a notion my interviewees assigned to them, or are they "software agents", a term that denotes agency? The (financial) literature on algorithmic trading occasionally draws an analogy between algorithms and robots, and this analogy points to software agents. Herein lies another potential transition we confront -the transition from algos to agents, from what is merely a tool and instrument for human traders to traders that act on their own but are algorithmic in nature. The latter have to be reckoned with as strategic parties in human interactions, the former will do their work mainly by tooling us up. They are our "aides" with only instrumental rights and obligations. When we call these tools software agents, we elevate them to a higher status and suggest that they, too, are autonomous entities of a sort that can sense the environment and respond to it on the basis of specified goals. One way to draw the line between tools and software agents is to revert back to the distinction between algorithms that are the "bullet" and those that are "the finger on the trigger" -between those that merely execute trading decisions and those that make their own "decisions" on the basis of programmed analyses and models. One prediction one can make is that both types of algorithms will exist alongside each other in the future. The increasing volume of trading conducted by software agents poses interesting social science questions which will need to be explored. How do we have to imagine the interaction, on a microsociological level, between a software agent and a human agent? Software agents lack the "deep" play of humans -their generative ways of playing meanings, emotions, and existence. Relationships with these agents will not have the same quality as social relations. But we are likely to project some of our social relational expectations and fantasies onto them, and they may engage and attract us. I have used the term postsocial for object relations of this kind and have suggested that such unfolding, information-rich entities that lack completeness of being (like a market) may lure us into such relations [START_REF] Knorr | Traders' Engagement with Markets: A Postsocial Relationship[END_REF]. The algorithm-based world that human traders and financial markets already inhabit, for instance, surely is a postsocial world. It's trivial to say that algorithms lack soul. But it's perhaps less trivial to find out what our postsocial relations with algorithms will look like in the future. When algos do the "touching", as I envisaged in the beginning of this essay, when they trade according to decisions they themselves

  simply lost their jobs, and it is unclear where they went. The new skill sets expected of human participants in hedge funds often involve a combination of quantitative skills and programming knowledge as well as trade design and fund management skills. Here is the wording of a recent ad accessed on the web in May 2014: A Hedge fund in Greenwich is seeking a Quantitative Research Analyst to join their Global Currency Portfolio Management team. This is a great opportunity to work on the cutting edge of quantitative investment & portfolio strategies for FX. Responsibilities will involve: 1) building trading strategies, tools & applications for global currency trading, 2) portfolio construction/optimization, and 3) FX/Currency exposure management. Applicants should have: 1+ years' experience working with economic and financial databases: very strong programming skills in Java, C++, SQL and Excel: a Ph.D. graduate or Ph.D. candidate from a Top School in Computer Science, Math or Financial Engineering: and superior communication skills.

Table 1 .

 1 Different kinds of "subjectivities"

	Human Actors	Software Agents
	Emotion	Unemotional
	Interpretation (taking role of other)	Scientific
	Discretionary judgements	Dumb
	Discipline	Seeing and reading
	Self-regulation	Regulation via risk assessment
	Research in delayed time	Research in real time
	Division of labour	Division of labour
		Seductive (fast and cheap)

  Onerecent example is a hacked Twitter message sent out by the news agency Associated Press on 23 April 2013: "Breaking (news): Two Explosions in the White House and Barack Obama is Injured".The message was wrong, but it caused the Dow to plunge by 140 points, the dollar to yen exchange rate to fall, and a downturn in bond yields, all within seconds. "No human believed the story. Only the computers react to something that serious disseminated in such a way", a Wall Street trading director said about the market's reaction. Human traders, seeing the selloff, checked the tweet against other, more reliable news sources and concluded the tweet was incorrect, and stopped the market plunge (Associated Press also quickly discovered the error).11 Human beings have a moral sense. They can regulate their own behaviour, and they tend to continually and effortlessly monitor rule compliance in others. The phenomenon that Wall Street does not conform to or even understand the moral expectations of Main Street is no contradiction to this assertion. Wall Street, too, has its morals, but its moral rules have little to do with what the external world thinks and wants. Wall Street morality is part of a law of trading practice, a lex mercatoria enforced by traders during trading. It's a law oriented to managing orderly behaviour among insiders without real concern for outsiders. Internal self-regulation is needed in markets in which there are no centralized national exchanges. Currency markets are a case in point. As overthe-counter markets, they are not subject to the standards and disclosure requirements of exchanges, which are much more regulated. Like derivatives markets and financial markets generally, currency markets lobby for self-regulation, and governments have set them free -partly, perhaps, in response to lobbying, but surely also to prevent the problems fixed exchange rate regimes tend to incur.Algorithms can be programmed to stop doing what they do under specified circumstances, but they clearly don't have the "moral compass" we attribute to humans. In fact, algorithms tend to be controlled by risk management techniques rather than by a moral sense and ethical considerations.

For a brief history of the respective competitions see the Wikipedia entry: http://en.wikipedia.org/wiki/Computer_chess (accessed 16 July 2013).

See the Foresight report[START_REF]Foresight: The Future of Computer Trading in Financial Markets[END_REF] (pp. 42-43) for the summary on which my discussion is based.

Market makers provide liquidity to the market, meaning they are at least in principle obligated to buy or sell when a request is made. They attempt to profit from the difference between the buying and selling price (the bid-ask spread).

For a fuller report, see the following article: www.dailymail.co.uk/news/article-2313652/AP-Twitter-hackersbreak-news-White-House-explosions-injured-Obama.html#ixzz2dxXoCW3t (accessed 18 August 2013).


Robert B Johnston
email: robert.johnston@ucd.ie

Kai Reimers
email: reimers@wi.rwth-aachen.de

Stefan Klein
email: stefan.klein@wi.uni-muenster.de

Performing Research Validity: A "Mangle of Practice" Approach

Keywords: Validity, performativity, truth, objectivity, significance, rigour, relevance, mangle of practice, infrastructuring

Mainstream discussions of research validity (truth, significance, objectivity) draw heavily on a certain "representational idiom" of science [1] that assumes a knowledge-reality correspondence. However, for research on practices, rather than nature, such a knowledge-reality distinction is neither feasible nor desirable, as it is at odds with the very notion of a "practice". Drawing on Pickering's alternative "performative idiom" for science, and extending it to participatory forms of social research, we propose alternative validity claims for practice-oriented research. Using the example of information infrastructuring practices, we show that the three aspects of validity thus reinterpreted become quite closely related to each other and also to the process of information infrastructuring itself. In so doing, we demonstrate the importance of extending the notion of "material agency" to embrace the dual agencies of the practice studied and the researcher's own disciplinary practice.

Introduction

Mainstream discussions of research validity (truth, significance, objectivity) draw heavily on a certain "representational idiom" of science [START_REF] Pickering | The Mangle of Practice[END_REF] that assumes a knowledgereality correspondence. Within this representational idiom, notions of research validity come to be formulated as dimensions of this putative knowledge-reality correspondence. In this paper, we ask: what should be the validity claims for practiceoriented research where it is neither feasible nor desired to adopt the assumptions that support the correspondence principle of the representational idiom?

We will argue that to answer this question we must shift the discussion of validity away from definitions that assume an observer-reality split and consider how validity is actually performed in the real-time, situated struggle of the researcher to bring forth and understand a phenomenon. We will seek to define validity in terms of attributes of the actual interaction between the researcher and the studied world. For this we use Pickering's performative "mangle of practice" framework [START_REF] Pickering | The Mangle of Practice[END_REF], extending its application to the case of participatory practice-oriented research.

As a concrete example of practice-oriented research we use an approach of our own design [START_REF] Reimers | Novice-ased Data Collection Methods for the Study of IOIS: Practice Probes and Learning Communities[END_REF] for researching information infrastructuring practices. We will argue validity in this case requires an account of research practice that fully acknowledges the realtime, emergent, social and material nature of both the infrastructuring practices studied and the researchers own disciplinary practice. We show that the three aspects of validity (truth, significance and objectivity) need to be radically reconsidered: when understood performatively they become quite closely related to each other and also, in a particular sense, to the phenomenon that is studied.

In so doing, we address the theme of this conference by showing how an issue normally framed within an epistemology of separation of knower and known obtains a richer solution when approached from a performative and interactive perspective. Specifically, we show that in the context of strongly participatory social research Pickering's notion of material resistance must be extended, and this extension has novel consequences for claims of research validity in this context. More generally, we reaffirm that Pickering's performative approach, by emphasizing that all scientific knowledge is acquired through ongoing real-time interaction between knower and known, negates any schism between natural and social science on the question of validity.

The flow of the argument is as follows. We begin by describing Pickering's critique of the traditional "representational idiom" for science and his alternative "performative idiom". Using the latter, we present an alternative formulation of validity that is applicable even when a knowledge-world separation is not feasible. This we apply to our example case of information infrastructuring practices, analysing what Pickering's "mangle of practice" would entail in this context and what novel insights a performative analysis of validity yields. We conclude by reflecting on the generality of our arguments.

2

The "Representational Idiom" of Science and its Problems

Pickering [START_REF] Pickering | The Mangle of Practice[END_REF] (p. 5) argues that there is a dominant representational idiom of science that "casts science as, above all, an activity that seeks to represent nature, to produce knowledge that maps, mirrors, or corresponds to how the world really is". Within this representational idiom, notions of research validity come to be formulated as dimensions of this putative knowledge-reality correspondence.

Truth: How well does the representation correspond to its real-world object? Objectivity: How independent is the representation of human interests and prejudices?

Significance: How broad is the class of real-world phenomena that the representation covers?

The disciplines of Organizational and Management Studies (OMS) and Information Systems (IS) have largely taken over this accepted view of research validity as part of their allegiance to science as a privileged basis for knowledge and as a basis for disciplinary respectability. This view is so embedded in research culture through the influential scientific realist philosophy (e.g. [START_REF] Weber | Evaluating and Developing Theories in the Information Systems Discipline[END_REF]), and also through the subject-object split implicit in everyday folk-theories of knowledge [START_REF] Spinosa | Disclosing New Worlds: Entrepreneurship, Democratic Action, and the Cultivation of Solidarity[END_REF], that research more generally tends to be held accountable against these dimensions of validity [START_REF] Bryman | Business Research Methods[END_REF]. 1 This is the case even for interpretive and practice-oriented research that does not by and large accept the ontological and epistemological assumptions underpinning this correspondence theory of knowledge, leading to complaints by such researchers that their work is often judged against standards they reject [START_REF] Klein | A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems[END_REF] (p. 84).

However, for research of a participatory flavour in these disciplines, because the object of study is "practices" (of organizing and IT use) it is not so straightforward to separate knowledge and reality in a way required by the representational idiom.

The first problem is the degree to which knowledge and research object can be separated in principle. If one takes seriously the notion consistent with the "practice turn" in OMS and IS [START_REF] Orlikowski | Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations[END_REF][START_REF] Schatzki | The Practice Turn in Contemporary Theory[END_REF] that practices are interpreted material and social performances, then what is researched in practice-oriented research includes how the practitioners themselves make sense of their practices -the very same practices the researcher is studying. This makes a separation of knowledge and observed reality to the poles of a correspondence highly problematic.

The second issue is the degree to which researcher knowledge and practitioner knowledge are entangled in practice. Far from being a passive object of study, the practitioners studied are members of the broader community that commissions sociotechnical research and are thus potential customers for the knowledge produced. Knowledge gained and made explicit by the research has the potential to become one of the drivers of change in the focal practices. This is particularly the case for practiceoriented research that has strong participatory or ethnographic flavour, such as our example. Furthermore, this recursive relation between knowledge and its object introduces an extra normative requirement that knowledge should be (at least potentially) useful and relevant to the practices studied. This additional validity requirement of relevance is frequently viewed to be in direct conflict with, or demanding a trade-off with, the standard validity attributes above [START_REF] Schön | Knowing-in-Action: The New Scholarship Requires a New Epistemology[END_REF][START_REF] Dubé | Rigor in Information Systems Positivist Case Research: Current Practices, Trends, and Recommendations[END_REF].

The attraction of the representational idiom of science is that it promises to keep the messy human process of knowledge production, as documented in numerous studies of the sociology of scientific knowledge [START_REF] Fleck | Genesis and Development of a Scientific Fact[END_REF][START_REF] Latour | Laboratory Life: The Construction of Scientific Facts[END_REF][START_REF] Galison | How Experiments End[END_REF][START_REF] Knorr Cetina | Objectual Practice[END_REF], separate from its knowledge "product". This knowledge product, a representation, then takes on the kind of independence of humans that its object -nature -is assumed in science to have, and validity is sterilized from any trace of the human agency and work that produced knowledge in the first place. However, the entanglement of researcher knowledgeseeking practices with practitioner knowledge-using practice outlined above threatens to contaminate the knowledge-reality separation underpinning the standard account of validity. Must we therefore accept that the validity of practice-oriented research can never match the "gold standard" provided by science?

3

An Alternative Account of Scientific Practice

We will argue that to answer this question we must shift the discussion of validity away from definitions that assume an observer-reality split and consider how validity is actually performed in the real-time, situated struggle of the researcher to understand phenomena. We will seek to define validity in terms of attributes of the actual interaction between the researcher and the observed world. Once it is understood that all scientific knowledge is acquired through such ongoing real-time interaction, any schism between natural and social science on the question of validity disappears.

For this purpose, we draw on Pickering's alternate performative account of scientific practice. Although it was introduced largely in the context of natural science, we will show that it can be used also to conceptualize validity claims for practice-oriented research. This is possible because this formulation of scientific practice does not depend on the usual realist assumption of an independent, separable research object to which knowledge must correspond, but rather focuses on "how, in practice, connections between knowledge and the world are made" [START_REF] Pickering | The Mangle of Practice[END_REF] (p. 182).

3.1

Pickering's "Performative Idiom" for Scientific Practice

Pickering [START_REF] Pickering | The Mangle of Practice[END_REF] points out that the representational idiom of science encourages a particular account of scientific progress as a gradual and inevitable revelation of a reality "out there", which he calls the "scientist's account". Importantly, the scientist's account takes reality to be described by our current understanding and then reconstructs breakthroughs that led to this understanding as an inevitable triumph of reality over earlier false theory (see also [START_REF] Latour | Laboratory Life: The Construction of Scientific Facts[END_REF]). He criticizes this account for failing to acknowledge that scientists (and actors in practices, more generally) actively make unforced choices and so exercise agency. The actual work that scientists do in achieving a successful capture of natural phenomena through tuning mechanical apparatus and routinizing operating procedures, in interpreting data, and in revising theories and beliefs, is deleted from the "scientist's account". The effort and temporality of this work is replacedthrough hindsight -with a story of unproblematic comparison between reality and representation in which both humans and machines are largely absent. Thus, nature is the only "agent" in the scientist's account.

By contrast, Pickering draws on extensive archival material and interviews on actual practices of scientists ( [START_REF] Pickering | The Mangle of Practice[END_REF], [START_REF] Pickering | Constructing Quarks: A Sociological History of Particle Physics[END_REF], see also [START_REF] Galison | How Experiments End[END_REF]) to show that both choices between rival theories and evaluation of data from experiment can only be understood as involving "human agency" -intentional human choices that are conditioned by historical and cultural context. Thus, Pickering seeks to provide an account of science that makes central the agentic real-time, performative nature of scientific practice.

The basic assertion is that practices are extended from one moment to the next through the coming together of human and material agency, a process that he calls "the mangle of practice", consisting of alternating episodes of resistance and accommodation. His exemplar case is high-energy particle physics, which employs increasingly complex and large machinery to capture certain physical phenomena and allow comparison with theory. Viewed in the real-time of actual scientific practice, the capture of phenomena is a time-extended struggle between the scientist's intentions and choices, and recalcitrant machinery. Machinery offers resistance as it produces unexpected or awkward results: the scientist tunes and retunes the machinery and/or develops new interpretations and concepts that would align with the results in an ongoing iterated process. To capture the dynamism of this process, Pickering proposes to view the resistance of the machinery as a "material agency" that struggles with the human agency of the scientist. A phenomenon is understood if and when the iterated process of resistance and accommodation converges toward a stabilized agreement between explanations and experimental outcomes. Any such stabilized understanding must be viewed as an outcome (a collaboration) of both human agency and material agency. Such a stabilization is neither guaranteed nor determined by initial human intentions nor material configurations. The process is thus open and emergent: there is no purely mechanical procedure that will inevitably direct the scientist towards "the truth". Any stabilization is hard won and fragile, at risk of coming apart again through further human or material agency. This idea is depicted in Figure 1. The "mangle" according to [START_REF] Pickering | The Mangle of Practice[END_REF]. Researcher's intentions encounter resistance from machinery, resulting iteratively in an accommodation of human and material agency.

Validity under the Performative Idiom

Pickering's performative account of science replaces the notion of a representationreality correspondence with a dynamic process of achieving a stabilized capture of natural phenomena in machinery (apparatus). Pickering [START_REF] Pickering | The Mangle of Practice[END_REF] (chapter 6) shows that traditional notions of validity based on correspondence must be revised and linked to the stabilization of the dynamic mangling:

Truth: Knowledge of a phenomenon is acquired if, and to the extent that, mangling of the researcher's agency with material agency becomes stabilized. This is not a correspondence of the scientist's abstract theoretical account with an external reality, but an agreement between a scientist's hard-won understanding of a natural phenomenon and how it is manifested in the performance of actual machinery. This then is a performative claim for truth.

Objectivity: As joint mangling approaches stabilization the researcher's original intentions get "mangled" by the resistance put up by machinery. The process of

Researcher Intention Material Resistance

Material Agency Accommodation Human Agency mangling thus enacts a performative kind of objectivity: human agency develops and evolves as a series of responses to the agency of a material "other", rather than from independently willed moves that might force initial human plans, interests and prejudices upon the outcome. The resulting stabilization might depend on the historical and cultural context of human agency but no longer on individual initial desires, intentions or free-will of the researcher. This then is a performative claim for objectivity.

Significance: Pickering did not explicitly consider research significance. It is reasonable, though, to associate significance with some measure of the breadth of the phenomenon that is captured in the stabilization. This associates significance with universality of the phenomenon studied, rather than generality of a theoretical assertion. We will return to a performative claim for significance later, in the more concrete setting of our example. 2

Moving Beyond Natural Science: Generalizing Material Agency

Our argument is that these alternate conceptions of validity developed by Pickering for the case of natural science can also be applied to forms of social science where close engagement of researchers with practitioners is involved. Firstly, these performative formulations of truth, objectivity and significance make no reference to a putative independent "real world". They therefore do not depend on a commitment to a realist ontology or an epistemology of separation between knower and known. They require only a genuine iterative interaction (mangling) of researcher agency and the resistive agency of the "machinery" that captures a natural phenomenon. Secondly, even though they were derived for the case where the phenomenon is captured in the machinic performance of experimental apparatus, they do not depend on this setting. Therefore, we can apply this analysis even when the phenomena investigated are social in nature and are captured in human social performance (human practices) where a realist separation of knowledge and world is untenable. Thirdly, the performative idiom captures how knowledge is actually accumulated, refined and tested in the face of, and actually as a result of, the resistance offered by the world to the researcher's knowledge-seeking intentions. This means that strong involvement of researchers with their object of study, characteristic of engaged forms of practitioneroriented research (including our example), does not preclude strong claims for research validity in social science.

However, any application of the mangle of practice beyond its natural science origins requires the notion of material agency to be generalized beyond the resistive agency of apparatus. Some extensions have already been considered by Pickering [START_REF] Pickering | The Mangle of Practice[END_REF] (see chapters 4 and 5) and in the next section we will extend the idea to the resistive agency of socio-material practices. First we will describe the general principle involved and then Pickering's own initial extensions.

It needs to be stressed that, even in the original experimental science setting, Pickering's notion of material agency is not referring to autonomous or cybernetic behaviour on the part of physical mechanisms. Rather, material agency is a product of the researcher-centric view he takes: seen from the experimentalist's situated real-time view, an apparatus that mediates access to nature presents itself as a combative "agent" enacting resistive "moves" in response to the researcher's intentional actions to validate proposed theory and concepts. The source of this agency is not any intrinsic intentionality of physical artefacts, but the researcher's situated involvement in the world. 3 Accordingly, Pickering has already extended the concept to cover forms of resistance other than from physical apparatus [START_REF] Pickering | The Mangle of Practice[END_REF].

The first extension is to resistance that is not primarily from the research object but rather from culturally entrenched notions of proper procedure within the scientist's own disciplinary practices, which he terms "disciplinary agency". The second extension ("cyborg agency") deals with resistance from a socio-technical assemblage (in his case, of numerically controlled machines and their human operators). In each case, it is shown that the "mangle" framework of alternating episodes of human accommodation to resistive agency, progressing towards stabilisation, adequately describes the case dynamics.

We can think of these two extensions as elaborating nuances of a broader concept of "materiality" defined as "that which resists human intentional agency" and which manifests as a "material agency" from the situated, performative view of an actor in a practice. It is this generalised notion of material agency that we will employ when we use the mangle of practice notion to discuss validity for our example case.

Example: Researching Information Infrastructuring

In this section we will illustrate how the performative analysis of research validity can be applied in a research project that would raise validity concerns under the representational understanding of science. Our example is a novel research intervention we have pioneered to study the process of industry-wide information infrastructure creation.

Information Infrastructuring

The study of information systems as information infrastructures is a relatively novel concern among OMS and IS scholars [START_REF] Tilson | Digital Infrastructures: The Missing IS Research Agenda[END_REF][START_REF] Bowker | Toward Information Infrastructure Studies: Ways of Knowing in a Networked Environment[END_REF]. Increasingly, scholars speak of "infrastructuring" rather than "infrastructure development" to emphasize the processual character of infrastructure creation [START_REF] Star | Steps Toward an Ecology of Infrastructure: Designs and Access for Large Information Spces[END_REF][START_REF] Star | How to Infrastructure[END_REF][START_REF] Pipek | Infrastructuring: Toward an Integrated Perspective on the Design and Use of Information Technology[END_REF][START_REF] Aanestad | Infrastructuring Work: Building a State-Wide Hospital Information Infrastructure in India[END_REF]. Our own work [START_REF] Reimers | Novice-ased Data Collection Methods for the Study of IOIS: Practice Probes and Learning Communities[END_REF], [START_REF] Reimers | An Empirical Evaluation of Existing IS Change Theories for the Case of IOIS Evolution[END_REF] has followed this processual turn. We view information infrastructuring as the ongoing becoming of infrastructure as diverse interacting practices become increasingly aligned in their pursuit of a shared enterprise, through on-going sense-making that may disclose new shared uses of information technology. This approach asserts that extension and deepening of the understanding of the possibilities for coordination among diverse practices drives an uptake of technology, rather than design and adoption of technological artefacts driving greater coordination.

To study infrastructuring we have devised and employed [START_REF] Reimers | Novice-ased Data Collection Methods for the Study of IOIS: Practice Probes and Learning Communities[END_REF] a novel research intervention called a Learning Community. In a Learning Community a diverse group of interested practitioners within an industry meet regularly over an extended period to engage in mutual and expansive learning about each other's practices. 4 The intention is that through such interaction, facilitated by the researcher, participants will come to understand the practices they necessarily depend on for coordinated activity, and consequently gain and diffuse back into their practice a deeper understanding of their own practice -including the potential of information technology as equipment for their practice. According to our hypothesis, this deepening and expanding understanding of interacting practices constitutes the process of infrastructuring. By facilitating and participating in the process, the researcher is well placed to gain an understanding of the infrastructuring process as it occurs. However, this understanding does not take the form of distanced observation: an important aspect of the researcher's role in the Learning Community is to discover by trial and error how to cultivate and nurture the community in such a way that the expansive learning that constitutes infrastructuring actually takes place. Thus in a very real sense, the researcher participates with the practitioners in the very infrastructuring process as it occurs. However, where the practitioners are motivated to enhance knowledge within their own practice by better understanding the practices they need to coordinate with, the researcher's motive is to increase scientific knowledge of the infrastructuring process being facilitated.

While this form of participatory practice research holds out the promise of authentic access to its research "object", it is also open to typical scientistic critiques of its validity, implicitly derived from the representational idiom of science. For example, since infrastructuring is an enacted process rather than an external, existent object how can any account of it claim to be true? Since the researcher sacrifices distance to participate in the process being studied how can an account claim to be objective? If a particular instance is studied how can the account claim to have any level of significance?

Applying the Mangle to Research on Infrastructuring

To apply the mangle of practice framework here we note that the researcher engages in a time-extended, two-way interaction with the Learning Community, comprising workshops, interviews and other forms of conversation and dialogue. The researcher will put up for review by the Learning Community a certain understanding of information infrastructuring that informs the researcher's interactions at a given time, which may encounter resistance in the form of objections or other negative reactions of the participants. Thus, just as in all other cases of mangling described by Pickering, the researcher will have to iteratively modify and accommodate intentions, understandings, procedures and instruments to a form of "resistance" until an agreement between researcher understanding and the reactions of the Learning Community is achieved. But it is important to recognise that this resistance derives from the understanding that each participant has by virtue of that participant's enrolment in their own particular practice, for instance, understandings within the practice of how work must be done, what are worthy aims and what is appropriate equipment use. Thus we style this kind of resistance as a "practice resistance", which is another form of generalised material resistance and appears to the situated researcher as a material "practice agency", not to be confused with the human agency of any particular participant. In fact, this resistance has the same nature and source as the resistance that participants face in their own attempts to adjust their own understanding of adjacent practices in the course of their exposure to these practice in the Learning Community. This process is quite analogous to how the physical scientist refines his/her understanding of a natural phenomenon by adjusting to how a machine that captures that phenomenon -their apparatus -responds to active "interrogation". In the present example, the phenomenon of infrastructuring is captured in the expanding understanding of the community of interacting practices. So again we can conceptualise research in this example as a time-extended struggle between the researcher's "human agency" and the resistive "practice agency" of the Learning Community as apparatus. This situation is depicted in Figure 2. However, the researcher not only faces resistance from the information infrastructuring practices, but also from his/her own research practice. The researcher is expected not only to apply "rigorous" scientific methods in his/her work, but also to extend scientific knowledge by doing sanctioned forms of research. Thus, the researcher must present to the research community new theoretical, empirical or methodological insights derived from the study of information infrastructuring in practice. These may challenge existing forms of knowledge and sanctioned methods, and will thus rouse "disciplinary resistance". The researcher is therefore necessarily involved in an additional mangling with disciplinary agency that must be stabilized if the researcher wants to validly claim to have extended disciplinary knowledge of information infrastructuring. This situation is depicted in Figure 3.

Researcher Inten,on

Prac,ce Resistance Prac,ce Agency Accommoda,on Human Agency These two forms of resistance interact with each other since some of the researcher's explanations that have been successfully aligned with the information infrastructuring practices may be resisted by the research practice and vice versa. The barriers to successful accommodation to these two intertwined forms of resistance are therefore quite high. In what follows we will demonstrate that such a call for dual accommodation is justified and, indeed, necessary for science genuinely to do its job in the realm of social phenomena.

Performative Validity Claims for Research on Information Infrastructuring

We now apply and extend Pickering's analysis of validity within the performative idiom of science to our example case of researching information infrastructuring practices.

Truth: An understanding of information infrastructuring is acquired if, and to the extent that, mangling of the researcher's agency with both the agency of the information infrastructuring practices studied and disciplinary agency of the researchers own research practice (Fig. 3) becomes stabilized. Such an understanding will be a hardwon, stabilized but fragile, agreement between researcher explanations and practitioner understandings. The researcher will have repeatedly and stubbornly presented their explanations of "what is going on" to the practitioners and within their own discipline to solicit responses. In this process, the researcher will have modified some explanations, dropped others and discovered alternative ones until both practice resistance and disciplinary resistance become sufficiently muted to assume that an accommodation has been achieved. The agreement is fragile because multiple practices are involved and articulation of that understanding can change the practices themselves. The agreement is hard-won because it resulted from the actual human work and acceptance of risk characterized by the mutual mangling process. The researcher will have put on the line their very identity as a social scientist, welcomed practitioners to challenge the researcher's grasp of the nature of their practices, and also accepted the challenge to defend that understanding to peers.

Researcher Inten,on

Accommoda,on If stabilization is achieved, it can be said that the researcher has "true" knowledge of the information infrastructuring process. It is worth digging deeper into the character of such a "truth claim". First, it cannot be a claim to have uncovered "mechanisms" that operate "behind the backs" of the actors in the practices, which are only recognized because of the special theoretical sensitivity afforded to the researcher by the research discipline. But equally, it is not a claim for special local practical knowledge by a researcher who has "gone native" and obtained tacit knowledge beyond the explicit reach of the discipline. Rather, it is a claim that an extended interaction between multiple parties and practices has created the ground upon which an explicit and truthful account of the infrastructuring process could be articulated, agreed upon and shared. On that ground of effortful and risky interaction, a knowledge-reality correspondence has been performed rather than passively observed.

Objectivity: According to Pickering's analysis, a performative interpretation of objectivity refers to a kind of "washing out" of individual interests, pet theories and normative orientations through the gradual and on-going mangling of human and material agency. Applied to research on information infrastructuring, the focal and the research disciplinary practices will have put up dual resistance to the researcher's intentions and will have forced the researcher to defend or modify theoretical concepts and methodological approaches to the phenomenon through several iterations. As this iterated process goes on, increasingly ideas and concepts will have become detached from their origins to the extent that the researcher can no longer claim to "own" them: nor can practitioners completely. They are now the "public property" of the collaboration. Consequently, these ideas and concepts can no longer be traced to particular interests, motives and preferences that researcher or practitioners might harbour. This process of freeing knowledge from individual intentionality is enhanced by the phenomenon studied that involves multiple practices and sources of resistance.

At the same time this new kind of "objectivity claim" can no longer serve to justify normative proposals to "reengineer" the practices to overcome problems, discovered by the researcher through objective causal analysis, that are not apparent to the practitioners themselves. However, it does support a different kind of normativity because the understanding so obtained includes and embeds the vision towards which the practices themselves are already heading and working as part of the becoming of the information infrastructure. Any advice that the researcher (or any practitioner) gives has already become at once both relevant and scientific, in a way that a privileged "interventionist" normativity cannot be.

Significance: Above we argued that significance, within a performative idiom, should be associated with the breadth of the interaction that becomes stabilised, rather than with the generality of some theoretical assertion. Such broadening can only be achieved through further mangling. For research on information infrastructuring, two possibilities might be entertained. In the first, styled on the traditional notion of generality, researchers might attempt to involve more practices in their efforts. However, such efforts would quickly become limited by material means and the difficulty of aligning ever more practices. The second is a possibility only revealed within the performative approach. An agreement already achieved with a particular infrastructuring process may be broadened through the mangling that occurs with research disciplinary agency, resulting in changes of understanding of information infrastructuring processes within the research practice itself. Other researchers, drawing upon this changed disciplinary understanding, may then encounter changed resistance when mangling their own understandings with further independent instances of infrastructuring practice, and so on, in an iterated form of Figure 3. Consequently, the multiple independent information infrastructuring processes studied by independent researchers will become indirectly involved in a broader mangling process. To the extent that the various understandings brought to this larger mangling process converge and achieve stabilization, the resulting alignment of understandings becomes broader and thus more significant.

Two things should be noted about the character of this performative significance claim. First, this catalytic form of broadened scope depends on the researcher submitting to both forms of resistance depicted in Figure 3. Second, this spreading and alignment of understanding within diverse infrastructures extends the reach of "public knowledge" among the practices, and is therefore the very process of information infrastructuring itself! Thus, the significance of research on information infrastructuring is measured by the extent to which the researcher ultimately contributes to the phenomenon studied.

Discussion and Conclusion

We began this paper by asking "what should be the validity claims (truth, significance, objectivity) for practice-oriented research, where it is neither feasible nor desired to adopt the assumptions that support the correspondence principle of the representational idiom?". Noting that Pickering's performative analysis of validity, developed largely in the context of natural science, does not require any commitment to scientific realism, we applied this analysis to our own context of research on information infrastructuring practices.

The answers we arrive at are both interesting and challenging. The three aspects of validity become quite closely related to each other and also, in a particular sense, to the phenomenon that is studied. A research claim is true if a genuine interaction between the researcher and practitioner has created the ground for an understanding shared between their practices; the claim is objective if it is not subservient to any party's particular intentions and interests and becomes "public property"; and it is significant if it has contributed to extending the reach of the information infrastructuring process. In a deep sense, these are also the requirements of the kind of shared understanding that constitutes the process of information infrastructuring itself. Thus for our example, performing valid research on information infrastructuring is largely indistinguishable from its practice object.

These specific conclusions about validity ultimately derive from our analysis of the dual resistance faced by a researcher from both the focal research practices and their own disciplinary practice. This dual resistance was not anticipated in Pickering's discussions of the mangle of practice because his cases (experimental, conceptual and sociotechnical) all still focussed on a researcher/actor studying a world they were not actually part of. However, the context of genuine participatory research motivated by the desire to create widely shared public practices by means of infrastructure, brings to the fore the need for an account of science as an activity of studying a world, or rather disclosing a new world [START_REF] Spinosa | Disclosing New Worlds: Entrepreneurship, Democratic Action, and the Cultivation of Solidarity[END_REF], that researchers and their research subjects are equally part of. This requires a more symmetrical treatment of scientific and practitioner knowledge production in which valid research is performed by meeting the challenge of this dual resistance and accepting the risk this entails. We argue this is the basis of genuinely engaged and relevant scholarship.

Thus, it is arguable that our conclusions also apply more widely, to a large class of social scientific research where the issue is contributing to public knowledge and broader practice. A reason for believing this is that all practices must of necessity coordinate with other intersecting practices and so all practice knowledge development is essentially infrastructuring. However, defining the class of research interventions to which our specific conclusions apply is for future work.

More broadly, the approach described here can potentially be applied to any research setting to determine specific conditions for research validity. The great advantage of this framework is that it does not require "special pleading" on the part of social science for validity, because the performative idiom is derived from, and already captures, the actual processes of knowledge creation in the natural sciences that are usually held to be the "gold standard" of rigour. An important implication is that research rigour need not be exclusively associated with observational distance. The issue is not for the researcher to retreat to the "high ground" of detached observation of the research object, but to create through effortful interaction with the research object a "middle ground" where a desire to account for the world and the world's resistance to being accounted for, can collaborate in performing rigour.

We also note a close relationship of our arguments to Gadamer's [START_REF] Gadamer | Truth and Method[END_REF] conception of "fusion of horizons" (between information infrastructuring practices and the research practice) and to Heidegger's [START_REF] Heidegger | Being and Time[END_REF] notion of truth as disclosure (aletheia) as the condition for the possibility of any knowledge-object correspondence. We leave these theoretical threads to be explored in future work.

Fig. 1 .

 1 Fig. 1.The "mangle" according to[START_REF] Pickering | The Mangle of Practice[END_REF]. Researcher's intentions encounter resistance from machinery, resulting iteratively in an accommodation of human and material agency.

Fig. 2 .

 2 Fig. 2. Researcher's intentions encounter resistance from information infrastructuringpractices, resulting iteratively in an accommodation of human and practice agency.

Fig. 3 .

 3 Fig. 3. Research accounts of information infrastructuring practices are an accommodation of human agency with both practice agency and research disciplinary agency.

Here we make a claim about what is experienced routinely within the OMS and IS disciplines. We recognize that there are other approaches to truth, such as coherence, pragmatic and consensus theories[START_REF] Mingers | Realising Systems Thinking: Knowledge and Action in Management Science[END_REF] but the correspondence theory appears to be the working folk-theory of truth in these disciplines.

In the language of dynamic systems theory[START_REF] Gleick | Chaos: Making a New Science[END_REF], truth is an attractor of the dynamic mangling process, objectivity is the insensitivity to initial conditions of the attractor, and significance is the breadth of the basin of attraction.

Thus he does not see material agency as totally symmetric with human agency, as do some "post-humanists"[START_REF] Collon | The Sociology of an Actor-Network: The Case of the Electric Vehicle[END_REF][START_REF] Barad | Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning[END_REF].

In this paper we will not present results of this research. Here we are using the particular arrangement of this research intervention as an example to which to apply the performative analysis of validity.