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  • Automated science I: Preliminary thoughts

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    One of the areas I want to explore more in the coming weeks is automated science over all — the idea that we could automate science, the entire process and perhaps the institution1 This is one of the interesting questions — what do we mean when we speak about automated science? Do we mean automating the process of science or the institution of science? Are these really different, and if so how? If we believe that it is the institution, then one question quickly becomes the conditions for and ability of us as citizens, individuals, humans to participate in science as a practice. Process, practice, institution — science can be described as all of these, and when discussing the automation of science we need to figure out which it is we are automating – or if it is all of them. is both exciting and somewhat worrying.

    These are some preliminary thoughts – written down mostly to map how I view the field before digging in more deeply into the literature and thinking that has already happened.2 I find it helpful to write out your own questions and ideas before digging into the literature, to have a naive mapping of the subject and how it seems shaped to me at this point in my work. This means that anyone reading this might be underwhelmed with ideas I find interesting – or frustrated that I am going over old ground, but methodologically this helps me enough that I am willing to risk that even in sharing these sketches.

    1. Let’s imagine a sciencomat. It is a machine that produces hypotheses, tests them, orders them into theories and explores the world all on its own. How would we design the user interface for such a machine? What would it need to be able to do for us to rely on it and trust its findings? Say we design it with a voice interface, much as a generative AI model today: we can ask it questions and get answers. Now imagine that this automat stands in a lab, and when we come to the lab in the morning it has a small red lamp that flashes if it has made a scientific discovery – and when it has discovered something we can then ask it what it has discovered. What kind of discoveries could such a machine make? What is the problem space it could operate in?
    2. Say we come in one morning and the machine blinks red. We ask it what it has discovered and it says « I have discovered a new fundamental force in the universe – and I call it xalitation. It is what produces gravitation in low temperature settings such as ours.» — how would we then proceed to validate this finding? And what if it answered « I have discovered a new drug that cures 30 different forms of cancer? » or if it said « I have a new theory about the fall about the Roman Empire»?
    3. One question that comes up quickly when we look at automated science is if it could ever really create the kind of paradigm shift that we saw from Newtonian to Einsteinian physics. And if it did if we would recognize it and find it legitimate.
    4. You could imagine a machine that finds different interesting mechanisms and functions in reality and shows how they can be exploited to reach fascinating results – but does nothing to explain those functions or mechanisms. A machine that produces engineering without science — and isn’t that what evolution is?
    5. Could evolution be automated, or would we be stuck in the conceptual frameworks we need to create the machine with which we would hope automate it?
    6. There is something here about observation. A machine could be made to observe according to different categories and templates – and then report on those observations. Automated observation seems to be the least controversial of all the steps in the scientific process. We could have a radio-telescope listen instead of us for structured signals that would indicate that there could be extraterrestrial intelligences — and if it recognized such a signal it could alert us. Here the machine is essentially engaged in a search in a pre-defined space of possible signals — something that could be easily automated.
    7. What about hypothesis-generation? A machine could, trivially, produce many random hypotheses. It is easy enough to imagine a machine that just randomly fills ut a pattern like « substance X causes disease X » – and then proceeds to test the resulting hypothesis space. In principle you would then only be dependent on the data available. In testing « liquorice causes lung cancer » we are left powerless if there are no data sets that document patients in the two dimensions we are interested in, liquorice consumption and the prevalence of lung cancer. 
    8. This suggests another kind of automated observation: large scale data collection on all kinds of possible variables in a hypothesis space: if we want to isolate the causes of cancer we should observe all the possible antecedents — but that isn’t possible. Not least because we could look at combinations of different substances and environments.
    9. Causality, then, quickly becomes a problem for any automated science. This is not surprising – since we know already that it is challenging for machine learning overall.3 As evidenced by the work Judea Pearl and others in e.g. Pearl, J., and Mackenzie, D. (2018). The book of why: the new science of cause and effect. NY, United States: Basic Books. But why do we believe causality is so important? Knowing that X causes Y makes it easier for us to understand the world – but what if we were offered a non-causal piece of knowledge that could cure a disease? Is this possible? There could be an ethics of causality somewhere here.

    There is much more to come back to in all of this. I am interested in also combining this first sweep with thinking about Stanislaw Lem’s ideas in Summa Technologiae on automated science – not least the necessity he sees in automation. There seems to be a set of interesting ethical questions here that could be worth exploring: is there an ethical imperative somewhere? Is it ethically defensible not to automate science? Assuming that automation is the only way to deal with the complexity or « megabyte bomb » that Lem speaks?

    Much more to dig into here.

    Footnotes and references

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      This is one of the interesting questions — what do we mean when we speak about automated science? Do we mean automating the process of science or the institution of science? Are these really different, and if so how? If we believe that it is the institution, then one question quickly becomes the conditions for and ability of us as citizens, individuals, humans to participate in science as a practice. Process, practice, institution — science can be described as all of these, and when discussing the automation of science we need to figure out which it is we are automating – or if it is all of them.
    • 2
      I find it helpful to write out your own questions and ideas before digging into the literature, to have a naive mapping of the subject and how it seems shaped to me at this point in my work. This means that anyone reading this might be underwhelmed with ideas I find interesting – or frustrated that I am going over old ground, but methodologically this helps me enough that I am willing to risk that even in sharing these sketches.
    • 3
      As evidenced by the work Judea Pearl and others in e.g. Pearl, J., and Mackenzie, D. (2018). The book of why: the new science of cause and effect. NY, United States: Basic Books.

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  • The study of the Game

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    This article from New Scientist was interesting. The idea that you can use AI to study old board games and infer possible rule sets, and so also maybe start to look at the genealogy of games overall – what some call the ludeme – is fascinating. All games are but one Game, and the Game represents something in us, a need, an evolutionary trait of some kind that encourages the ludic. With Huizinga’s notion of Homo Ludens, this does make sense – in many ways. Of course, the way we conceive of the game may be more or less harsh. I recently wrote the below on Cormac McCarthy vs current day philosopher Nguyen.

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    “At hazard” – an alternative view of games as social transformation

    Draft
    Nicklas Berild Lundblad

    Introduction

    In his book Games: Agency as Art (2020), C Thi Nguyen argues that games can be socially transformative, and that by trying on different agencies we can also try on different socialities – exploring what cooperation could look like in different contexts: 

    “Games are one of the oldest artifactual practices we have. When we play games, we take on a wide range of alternate agencies. Games help us to understand new forms of agency, and to understand those new forms from the inside. And if we accept all that, then it is not so strange to think that games can help us develop, change, and transform our social structures, by helping us to explore, from the inside, alternate social structures. Such explorations can help us get a handle on our own social structure, and show us what it might be like to operate within a new one.”

    Notably Nguyen’s view of agency is one where we have a primary, outer full agency that can then be layered, temporarily, into an inner agency in the game. This game agency can then be abandoned at will. This ability to inhabit different agencies is then extended to different kinds of social structures as well, built from the inhabited agencies. The dark side of this, he notes, is that we can be captured by games through either intentional or accidental gamification or lulled into complacency by the value clarity in games. 

    An intriguing counterpoint to this view is found in Cormac McCarthy’s Blood Meridian (1985). In one reading of McCarthy we find instead the following view: that man only has agency within a game, and that the worth and quality of that agency is commensurate with what is at stake in the game. Games are socially transformative, but they structure society by producing a rank order that then translates into conflict, and ultimately war – because war is the ultimate game. 

    In this essay I intend to explore and construct a defense for that claim, not in order to endorse it morally, but to find if it can yield any further insights into the nature of games and alternative views to those of Nguyen.   

    “Men are born for games.”

    Judge Holden is a towering, enigmatic figure in Cormac McCarthy’s 1985 novel “Blood Meridian.” McCarthy describes him as a hairless albino giant standing over seven feet tall, he exhibits a chilling mix of extreme intelligence and brutal violence. As part of a group of scalp hunters in the 19th century American Southwest and Mexico, the Judge displays vast knowledge across multiple disciplines, speaks several languages, and engages in philosophical debates, while simultaneously participating in horrific acts of violence. His complex character – at once refined and savage – combined with his mysterious nature and ambiguous motivations, has made him one of the most memorable and disturbing antagonists in modern American literature, often interpreted as a personification of evil itself or the modern world. 

    In one famous scene in the book, Judge Holden expounds the bare bones of a theory of not just games, but the nature of human existence as well. The section is worth quoting in full: 

    Men are born for games. Nothing else. Every child knows that play is nobler than work. He knows too that the worth or merit of a game is not inherent in the game itself but rather in the value of that which is put at hazard. Games of chance require a wager to have meaning at all. Games of sport involve the skill and strength of the opponents and the humiliation of defeat and the pride of victory are in themselves sufficient stake because they inhere in the worth of the principals and define them. But trial of chance or trial of worth all games aspire to the condition of war for here that which is wagered swallows up game, player, all. 

    Suppose two men at cards with nothing to wager save their lives. Who has not heard such a tale? A turn of the card. The whole universe for such a player has labored clanking to this moment which will tell if he is to die at that man’s hand or that man at his. What more certain validation of a man’s worth could there be? This enhancement of the game to its ultimate state admits no argument concerning the notion of fate. The selection of one man over another is a preference absolute and irrevocable and it is a dull man indeed who could reckon so profound a decision without agency or significance either one. In such games as have for their stake the annihilation of the defeated the decisions are quite clear. This man holding this particular arrangement of cards in his hand is thereby removed from existence. This is the nature of war, whose stake is at once the game and the authority and the justification. Seen so, war is the truest form of divination. It is the testing of one’s will and the will of another within that larger will which because it binds them is therefore forced to select. War is the ultimate game because war is at last a forcing of the unity of existence. War is god. 

    Brown studied the judge. You’re crazy Holden. Crazy at last. 

    The judge smiled.

    The focus on games is not accidental but feeds into a larger set of themes in McCarthy – around agency and humanity’s role in the universe. A reading of this as connected to the philosophy of games thus is not unreasonable. Fredrik Svensson, who in his dissertation studied the original McCarthy manuscripts, found a note next to this section that indicates that McCarthy was indeed interested in theory and philosophy of games here – it is a short reference to Homo Ludens – Johan Huizinga’s seminal work on games.,

    Judge Holden claims that men are born for games – indicating that game playing is not just a voluntary activity we can take on, but rather an essential activity we need to engage in. One key reason for this, he notes, is that it is in the game that we can determine our worth. 

    One reading of this is to say that Holden’s theory is based on the idea that games are mechanisms for creating meaning and making sense of the world, as well as creating an order to the world at large – one in which there is a distinct ranking of everyone according to wins and losses. We find, in Holden, then distinct echoes of a Nietzschean worldview – one where a game is primarily agonistic, a struggle between wills, and the outcome and impact of that outcome is key. 

    In contrast to Nguyen’s theory of striving play, or even the competing theory of achievement play, we here encounter a view of play as an ordering and ranking mechanism. We do not play for the striving, nor do we play for the achievements that we can showcase, but we play to produce an order to the world, according to the worth we can accrue in the game. 

    That order, in turn, is strictly determined by the worth of what is put at stake. 

    “…the value of that which is put at hazard”

    For Judge Holden games are not just pre-lusory goals and constitutive rules that allow us to pursue lusory goals in striving play. The defining property of a game is not the play itself, but what the game is about – or, as he phrases games are about “the value of that which is put at hazard”. This notion, that games have to have something at stake, seems at first blush to be a much more narrow definition of games than that used by Nguyen. Nguyen explicitly suggests that we do not have to care about the prelusory goal, it can be quite meaningless – like putting a small white ball in a hole somewhere in a prepared landscape. Would Holden, then, suggest that golf is not a game? Not necessarily, but he would suggest that the prelusory goal is meaningless – and what matters is the vanquishing of the other players – “the humiliation of defeat and the pride of victory” is what matters in golf, not the actual objective of putting the ball in the hole. 

    Holden’s perspective forces us to focus on what the prelusory goals actually are. Nguyen’s suggestion that these goals can be meaningless in themselves – that the goal can be to put a golf ball in a hole, say – is simply wrong. Games are about producing rank and all games have as their prelusory goal some kind of status struggle. 

    This seems to suggest that Holden sees all games as achievements, but even that is not quite right. The theory here is not that it matters to win, but rather that it matters that you win because of the effects that winning produces, the resolution of the wager, the loss or gain of that which is put at stake. It is certainly not striving play, in the sense Nguyen uses the term, but it is not quite achievement play either. If we were to try to name this kind of play, it seems natural to call it impact play. Holden does not care about achieving the win, he cares about what the win does with the players, how it sinks one in defeat and raises the other in victory – the impact of the win, of winning or losing what is at stake, is what defines the game.

    There is a deeper point here, possibly, and it has to do with Nguyen’s and Holden’s views of agency. For Nguyen, we can voluntarily inhabit agencies or socialities – whereas Holden’s view seems much more constrained. In Holden’s rendering of games, we are not free to play or not play, but locked in a constant struggle for power that plays out in games of various kinds – the game is the form of that power struggle, and that is why there is always something at stake. When we are playing a game we are locked in a conflict, rather than consenting to a contract.

    How would Nguyen react to the game suggested by Holden, where a turn of the card – say black or red – decides life or death for one of the players? Would he argue that this is not a game at all, and if so why? Nguyen could insist that for something to be a game, we have to be able to move back from the agency we inhabit – and in the case of this lethal card game there is no such chance of backing out. This suggests an interesting extension to Nguyen’s idea of games, where for something to be a game it has to leave everything in our primary world as is – a kind of forced acausality, where the game should not change anything in real life. Russian roulette or Holden’s card game violate that rule.  

    Nguyen’s perspective here seems to be deeply idealistic – and if we examine the way that games are used in society, the rank order they produce, the social divisions they uphold – the evidence seems to suggest that Holden’s view is the more robust one. 

    Nguyen’s focus on striving play does not explain our obsession with lists related to games – we keep a record, we order and we rank in sports and in games, and even when we play chess we keep a count of who won the most times. For Holden this is evidence that games produce order, worth and rank. Holden’s view of games is more consistent with the social transformation of society into lists, orderings and ranks that seems to be a key component of game playing.     

    “War is the ultimate game…”

    Holden’s assertion that war is a game, the ultimate game, plays into this view of games as being about their outcomes and impacts – and also suggests that games are not quite as voluntary as suggested by Nguyen. It seems likely that Nguyen would recognize the similarity between war and games, the forced trust between soldiers and forced competition between persons who may not have anything against each other at all – there is in war, as in games, a social transformation of relationships. Another aspect of war – as a game – is that war also requires a layered agency, albeit in a much more horrific way than in a game. You have to move from seeing other people as human beings, to seeing them as threats – and your goals become singular in the field: survival is all. 

    But Nguyen would probably not agree that war is a game – and largely because of his emphasis on the voluntary nature of game playing – we consent to the game. Rather, this is an example of what he could call intentional gamification, and as such it is potentially very harmful.   

    Judge Holden is only concerned with agon – games of competition and conflict, and the only real games, for him, are games where the competition is about something that is worth putting at stake. It is not so much, then, that war is a game, as all games should have a little war in them. That element, the agonistic, is all-dominating in Holden’s view of games – and there is rich historical evidence to connect games and war together, from a purely genealogical perspective. This evidence is further strengthened by the dominance of different war-themed games in video gaming, and the focus on conflict in these games – but Nguyen could, rightly, object that even though the genealogy may look like this, the nature of games may well have morphed and mutated into something much broader and richer. The deep history of games may need to be sought in conflict and war, but they evolved to become something else, and more complex and what Nguyen is arguably trying to do is to explore this broader class of games. 

    The question of whether war is a game is not merely a classificatory exercise, but also requires that we rethink what it means to participate in a game, and how voluntary games are – and what it is that they give us in return. There is, in Judge Holden’s view, a moral and social transformation of man in games as well – but it is one that is far darker than in Nguyen: they reduce us to an almost Hobbesian state of nature, where what is at stake defines us. 

    Yet – denying that war is a game, or the roots of all – or many – games seems hardly realistic. A theory of game playing needs to account for the relationship of war and games, and not just define away war out of convenience. Holden’s view of games includes war as the ultimate game, whereas Nguyen’s struggles to classify war at all.

    Agency in McCarthy and Nguyen

    The tension between Judge Holden and Nguyen’s philosophy of games to some degree is, then, rooted in two very different conceptions of agency. Nguyen assumes that we have agency, and that we can shift between and inhabit different agencies through playing games. 

    In some sense Nguyen’s theory of games is based on a sense of readily available, open agency that can be directed at will – and is not constrained by others. Games allow us to inhabit new agencies, and we can move between the layers in agency by choice. Nguyen’s agency is the agency of a sovereign individual. 

    The key elements, simplified, of Nguyen’s view of agency are the following: 

    (I) Agency is fluid, temporary and malleable. Nguyen’s view of agency is remarkably open. We direct and shape our agency, we can inhabit other agencies and we can even aggregate agencies into socialities.  

    (II) Agency can be layered. This view of agency as consisting of layers allows Nguyen to argue that there is a moral gap between what happens in the game and what happens in the real world – but it also indicates that his view of agency is one where it can be divided.  

    (III) Agency is socially shaped and can be captured in games. This is the key to why games can be socially and morally transformative in Nguyen’s theory – and why we can access games as libraries of agencies. 

    McCarthy’s views of agency are more complex, and the character of Judge Holden is a key example of this – starting with the fact that Holden is a judge, that he is so closely associated with the law, and the determinism of a barren universe. We find through-out Blood Meridian and McCarthy’s other works, an occupation with agency and the possibility of agency, and the view that emerges is not that of a flexible and temporary agency, but almost a curious reversion of Nguyen’s model. If there is a layered agency in McCarthy, then the gaming agency, the lusory attitude, is the primary one in terms of how meaning is constructed. The immersion, the moral simplicity – not clarity – that the judge represents in the story, his view of man as made for games, and nothing else, all point to a view of agency as weak as best, non-existent at worst, outside of the game. Man is locked, then, into the simplicity of the game and the inversion here is complete: we are all playing a game for money, meaning and status, and if there is a “real” agency it is not accessible other than through an act of rebellion against the systems that program us. Even more likely, then, that there is no agency at all outside of the confines of the game.  

    The tensions between Nguyen’s notion of agency and the views exhibited by Judge Holden translate into a question about the existence of agency outside of games, but also the order of the layers that Nguyen suggests that we can move between – is the lusory layer actually the primary one, and does the outer agent exist? 

    Nguyen puts the question of agency at the heart of his theory, and suggests that games are agency as art – but for that to work, there has to be a very special kind of agency, one that is not just layered, but also to some degree ordered in a certain way, and ultimately free. Judge Holden’s view of games denies that there is such free agency, and if there are layers, then we are marionettes playing an economic game of violence, and there may be the possibility to revolt against that – but that revolt has the flavor of the absurd, the impossible. It is only within the game agency is truly possible, because it is only there worth is produced.   

    Conclusions

    One way to look at this is to say that both Nguyen and other philosophers who analyze games, like Thomas Hurka, represent a kind of Aristotelian view of gaming as a way to self-realization. The merits of engaging in striving play and achievement play are directed mostly towards the player, and both conceive of the value of playing games as largely positive and voluntary. For Nguyen losing is a part of playing, and not an essential property of the game, and for Hurka it is the unfortunate side effect of not winning – but allows for practice,growth and another shot at winning. For Holden, however, game play is the exercise of power, and the point of the playing is to make the other side lose, and to win what is at hazard and produce worth and order in a largely nihilistic world. This represents an almost Nietzschean view of game playing, very distinct from the one represented by Nguyen and Hurka. 

    Judge Holden’s theory of games picks up on something unsavory in the act of game playing that Nguyen avoids – the fact that someone loses and that this loss may have a value to the winner, and may in fact change the power dynamics between the two players in different ways that extend outside of the layered agency. Games produce rank. His outlandish proposal that games have their ultimate form in war is a version of this: for Holden the primary objective of fighting a war is not to win, but to ensure that the other party, the enemy, loses. Here his psychological model does seem worthwhile to explore, as terrible as it is, since we see time and again that we use games to produce order and rank in society – in both war and games. Why the popularity of game shows where we vote participants out and with lists of economic successes?  

    What we can learn from Judge Holden, and Cormac McCarthy, is that to understand games we must see them as much more transactional, and as more of a cost imposed on the loser, rather than a benefit accrued by the winner. Games, and play, may inherently be much more informed by Nietzsche’s will to power, than by a search for pure entertainment or striving play. But Judge Holden goes further – the agon that Nietzsche discusses in, for example, the fragment Homer’s contest, is in some sense constructive. Judge Holden’s contest is strictly zero-sum, or even minus-sum, where even the winner has something taken from them – their humanity, perhaps. Games have, in them, a kind of cruelty that when it takes over the game can make the gaming environment toxic and antagonistic – as evidenced by researchers in game studies. 

    Judge Holden’s view of games, then, presents a stark contrast to Nguyen’s theory of games.  

    Agency is achieved only when we enter into the game – since that is where worth is produced and meaning is had. The idea that we have agency without agon, for Holden, is simply wrong. This also suggests that Nguyen’s model of an outer, primary agency is wrong – there is no agency outside of the game. 

    The voluntary and exploratory nature of games that Nguyen suggests we find whenever we study game play is largely illusory. Games are mechanisms of ranking and ordering the world, and the most important socially transformative role they have is that. To imply that we have freedom to dream about other worlds and other societies in games is idealistic and not supported by the evidence: when has a game ever led to a social change? It is telling that Nguyen has no such examples to point to, whereas Holden theoretically could point to games producing order, lists, ranks everywhere – the social mechanisms of sports alone would suffice to make his point. 

    Nguyen’s philosophy of games is written from a voluntary player’s perspective. Holden’s theory of games is written from the perspective of the forced participant, locked in a contest of wills, competing about what is at stake to produce and prove their own worth.   

    Overall, even if Nguyen does explore problems with games and gamification, Judge Holden reminds us that there is a much more grim reading of games and game playing. If we ignore this darker side of games, we will not have understood them completely. Nguyen’s view of games as social transformation is hopeful, suggesting that we can inhabit socialities of different kinds. The view we find in McCarthy is deeply pessimistic, and suggests that the key social transformation inherent in games is a legitimizing of violence and conflict, giving us agency only to produce rank and order.

    The views ascribed to Judge Holden here are intentionally extreme, to tease out an alternative view and a contrast to Nguyen’s thinking – a more pessimistic, and perhaps more bleak view of games than that presented by Nguyen. A further analysis might suggest that there is interesting middle ground to be explored here as well – and that is worth returning to. 

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  • Incremental writing

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    Lately I have run into the concept of incremental writing, and I quite like it — the idea that there are ways to write a little to write a lot. Well, duh – I know this is not new, but at least I tend to forget this and think that I need to sit down and write something that is 10 000 words or more to really feel that I have written. That is not the right way to approach writing overall, however. So how can this be implemented? I am already toying around with Obsidian and Notion for this purpose, and should continue to explore it.

    Other things on my mind right now.

    And formally time for Christmas songs now!

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  • Philosophy of games – essays

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    I just finished a course in the philosophy of games at Umeå university, and it was quite excellent. The course involved a lot of writing and I had opportunity to write two different long(er) essays that I quite liked. I am including links to them here if anyone is interested. They are obviously still works in progress, but it was nice to write longer form.

    Adaptive play -longDownload
    Computation, games and art – longDownload

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  • Philosophy of games I: on game blindness / deafness

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    If we assume that games have evolved for some function, that they have some practical use to play (as per Ruth Millikan on concepts in general), we can think about the ability to cast the world in a game as a special kind of skills or capability, and as for any capability or skill we can also imagine that there are those that do not have it at all — the tone deaf, the color blind, the face blind…there are numerous examples of such “blindnesses”. But what would that look like? What would it mean to be gameblind or gamedeaf?

    We could argue that it is simply not enjoying games, or being unable to engage in what Thi C. Nguyen calls “the motivational inversion” required for gaming.1 See Nguyen, C. Thi. Games: Agency as art. Oxford University Press, USA, 2020. This inversion, Nguyen argues, is the ability to shift from pursuing the means for an end, to purse the means as an end in themselves. Nguyen writes: “In ordinary practical life, we usually take the means for the sake of the ends. But in games, we can take up an end for the sake of the means. Playing games can be a motivational inversion of ordinary life.” Being unable to understand or execute the motivational inversion would then be another example of game blindness.

    If game blindness simply means to enjoying games at all, not seeing the point of engaging in play or no emotional or intellectual return on the invested time, well, then we could try to find out how many people regularly play games. Statistics here are not robust, however. Some indicate that there are billions of video gamers in the world2 3.22 billion gamers world-wide for just videogames, according to https://whatsthebigdata.com/number-of-gamers/ , and others suggest that roughly 21 percent of US adults play boardgames once a month3 See https://wordsrated.com/board-games-statistics/ , also arguing that 47.2 percent play word games. , but these statistics are not necessarily collected in such a way as to allow us to answer the question with any certainty. Further complicating this, most people seem to suggest that the reason they do play games is to socialize, and not to play the games for the sake of the games themselves. So, even if they were in some sense gameblind, or unable to enjoy games, they might enjoy the second-order benefit of socializing.4 See https://wordsrated.com/board-games-statistics/ – noting that more than 50% enjoy games for the social aspects.

    Could there be other kinds of gameblindness? What would that look like? An inability to understand rules and goals in games? Or could it be more subtle and related to not being able to understand what a game tries to communicate? Nguyen, again, suggests that games communicate agency:

    Games, then, are a unique social technology. They are a method for inscribing forms of agency into artifactual vessels: for recording them, preserving them, and passing them around. And we possess a special ability: we can be fluid with our agency; we can submerge ourselves in alternate agencies designed by another. In other words, we can use games to communicate forms of agency.

    Now, what if we could not read games as agency, would that come closer to being gameblind? We see the game, play it, enjoy it – but there is something that is not picked up by us because we lack the ability to perceive the inscribed agency? That seems hard to imagine — what would an example look like? Say I play chess, and I am unable to pick up on the combative agency inscribed in the game, I think it is collaborative – would that be an example of gameblindness? Say that I was unable to see the opposing King as an enemy, and instead invited them in by playing badly — would that then be an example of misreading the game?

    This scenario presents an interesting experiment: what would happen if you purposefully played in such a way as to allow the opponent to win — could they perceive that? And what if they started to do the same? This variation on chess exists – it is sometimes referred to as Losing Chess5 See https://en.wikipedia.org/wiki/Losing_chess , and is in itself one of the more popular variations on chess. If I play Losing Chess and you play Chess, we are not so much gameblind as merely unable to communicate about what the rules are, and what game we are actually playing – so that seems wrong — but what about someone who could only play one variation? Who could not imagine playing ordinary chess, but only played Losing Chess? This seems so contrived as to be impossible, but would they not suffer from some kind of gameblindness? Or would this just be a simple variation on aspect blindness?

    Being unable to shift into the agency recorded in the game, and taking on the role the game requires would come close to what we are looking for, but it would not quite be it either.

    Footnotes and references

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  • The Ozymandias test

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    Here is a simple test to apply to any project that you are asked to participate in, or that is proposed for your review: ask what the difference will be in two years time if we engage in this project and then ask what will happen if we do not do it. It is – intentionally – an annoying question, because it forces you think through what will actually change in a more substantial way because of the work you put in – and so can help you prioritize.

    Why two years? Well, you don’t have to stop there — you can ask what the overall change will be in 10 years, or put the bar even higher — you could ask what will be different in a 100 years time if you do this. Now, this becomes a kind of spiritual exercise, since there are very few things you can do that will matter in a 100 years – and it is not even obvious that this bar makes sense; but it forces you to look at things and realize that what you do will matter in some timeframe.

    There is some comfort in this, and a fair bit of sorrow as well — but I find it important. It reminds me of Shelley’s poem about Ozymandias:

    I met a traveller from an antique land,
    Who said—“Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away.

    At last, we all will be Ozymandias, no matter how mighty we are. You could argue that this is an argument against the two year question: why care about impact in two years, if there will be no impact at all in a thousand years? If all of our mighty, or perhaps, more likely, somewhat ok, works fade into the sands of time anyway?

    There is some truth to that, but I do think that the two year test allows you to put effort into things that you can observe having a difference, and that is in itself a kind of pleasure. To see the world change because you are in it, to understand that you are an agent of change and, hopefully, improvement. So the two year test is really not meant to sound important, or to be annoying, it is a way to help us all work collectively towards change such that we can enjoy it, look at it and say “we did that”.

    This is a privileged question, to be sure: many people do not have the opportunity to create things that might matter in two years in their day-to-day job — but if you do, you sort of owe it to yourself to figure out what makes a difference. I suspect that there is a lot of work in organizations that is channeled into maintaining a kind of organizational homeostasis: ensure that there are enough meetings on calendar, that the budget is spent, that the emails are written – because this is what it means to sustain the organization as organization. Without those meetings, that spend, the emails — there is no real organization. This is when works becomes less oriented towards change, and more oriented towards homeostasis. I suspect it often happens in very large organizations, to some degree – in pockets. The two year test can help you sort out if work is sustaining homeostasis or if it is impactful.

    And I don’t mean to ridicule homeostasis work — I think we just find it less satisfying. Keeping The Lights On or Business As Usual is actually enormously important, and it actually is true that organizations need some level of that to maintain a sustained existence over time. Not all the collective intentionality that an organization consists of can be directed outwards, towards change – some needs to be directed towards ensuring that the organization continues to exist. But if it is, make sure it is understood, and perhaps also limited to what is needed for the organization to maintain its identity and coherence?

    Asking what happens if we do not do this, is a good way to check another tendency in organizations – not looking at what will happen if you do nothing at all. Often someone else will step into the role you envisioned for yourself, and sometimes that can be acceptable. Sometimes you want the work and the role, and so you want to be the one to do the work.

    Strategies to engage with time matter – and finding the right questions to do so can help. The Ozymandias test can be used to assess how we use our time. Look at the work you do now — for how long will it matter? How long a shadow does this work cast? Is it 6 month work? 2 years? 10 years?

    As I look at my own work, it varies. Some work matters very little, and is essentially just heat, without impact. Some work does have a longer shadow, and I feel very different when I engage with it. I think I would like for that horizon to become longer, more complex, and matter more as I grow older. This may be a deceit; a wish to make a mark that betrays a certain vanity. But it could also be a sense of a broader engagement with the world.

    I am not sure which it is, but am hoping the latter.

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  • Notes on the philosophy of science II: Guessing as a skill

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    Can you become better at guessing? At a first glance the answer may seem an obvious yes – some people guess badly, others better – but exactly how would you go about that? What are the subskills that make you a better guesser, and can you practice them in some interesting way? Could their be courses in guessing better? If so, what would they teach and would we put them on a resumé?

    A few distinctions seem in order — how does a guess differ from a prediction, for example? We know that some of the skills that go into predicting future events also are important for guessing (such as the ability to break a problem into component parts and assess those component parts, as well as respecting baselines in making estimates) – but does this mean that guessing and predicting is the same thing? One possible distinction seems to be that guesses can be about the past and the present as well as the future – but you could argue that a guess is just a prediction of what we will find when we check something more thoroughly. A guess also seems closely related to an estimate – but maybe an estimate is more numerical? A guess seems broader — the etymology of the word is uncertain, but could be traced back to “to take”, and in that sense guessing is to take something as a given, perhaps.

    What is the size or nature of a guess? What is it that we guess about? We have some of the same problems with predictions – but we seem to say that we predict events rather than anything else. If that is the case, then maybe a guess is about the way the world is – a state of the world overall? Or is a guess more tightly connected with story and narrative? When we guess, we sort of assume or take a state of the world as a given and act on that assumed state, and the guess informs our understanding of the whole of our world?

    That might be reading much too much into the word guess, however. It seems likely that guesses may be both narrow and oriented around facts (patterns!) or broad and essentially represent world states. Are guesses more primitive than predictions and estimates? Is there less precision in a guess?

    And, so, to the point: can a guess be scientific?

    You could imagine an argument that essentially says that science has its origin in guessing, and that guessing is about how we think the world works. As guesses got more robust and rigorous and we developed guessing as a skill, it morphed into prediction and estimation. Guessing, in this telling, is more primitive and animalistic than any of the other practices we have mentioned.

    So, do animals guess? This turns out to be the subject of an interesting set of experiments – the guesser knower tests – and something that is used to discuss whether animals have a theory of mind. 1 Povinelli, D.J., Nelson, K.E. and Boysen, S.T., 1990. Inferences about guessing and knowing by chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 104(3), p.203.

    Povinelli et al describe an experiment investigating whether chimpanzees can understand the difference between someone who has seen an event occur (the “knower”) versus someone who has not (the “guesser”). Four chimpanzees were tested using an apparatus where food was hidden in one of four cups. One experimenter (the knower) observed the hiding, while another (the guesser) did not. Both then pointed to cups, with only the knower pointing to the correct one. Three of the four chimpanzees consistently chose the knower’s cup, even when the procedure was altered so the guesser remained in the room but covered their head during hiding. The authors interpret these results as evidence that chimpanzees may understand that seeing leads to knowing, and can distinguish between someone guessing versus someone who actually knows information. This ability is compared to perspective-taking and theory of mind capacities that develop in human children around age 3-4. The study provides preliminary support for the idea that chimpanzees have some level of attributional abilities regarding others’ mental states, though further research is needed to fully characterize the extent of these capabilities.

    The challenge with this is that guessing in the experiment is essentially reduced to being forced to choose at random — the guesser can not, in this experiment, become better at guessing in any way – can they? And what we wanted to figure out was if guessing could be a skill, and if guesses can be scientific — the answer, based on this framing, seems a clear know.

    A guess does not feel like a random pick, though. It feels like something more, based on something else. But on what? Previous experience? Narrative understanding? Intuition? This is where it gets tricky — if a guess is based on something we have to be able to define that something to some extent — and we could then start to think about the limits of a guess, when a guess is clearly not a guess anymore. If you ask me how many coins I think you have in your pocket, and I answer “ten geese”, well, I am not guessing at all. A guess delimits the set of answers we will accept at least in some dimensions (at least the famous educated guess does).

    And this turns out to be interesting – since there is some connection between the guess and us as organisms – we project ourselves into our guesses in ways that make them more than random. An interesting recent paper by Myer and Madrid highlights this:2 Myers, J.M. and Madrid, H., 2024. Synchronizing Rhythms of Logic. arXiv preprint arXiv:2405.20788.

    Although quantum states nicely express interference effects, the outcomes of experiments are not states. Instead, outcomes correspond to probability distributions. Twenty years ago we proved categorically that probability distributions leave open a choice of quantum states to explain experiments that is resolvable only by a move beyond logic, which, inspired or not, can be characterized as a guess. Guesses link the inner lives of investigators to their explanations of experimental results. Recognizing the inescapability of guesswork in physics leads to avenues of investigation, one of which is presented here.

    We invert the quest for the logical foundations of physics to reveal a physical foundation of logic and calculation, and we represent this foundation mathematically, in such a way as to show the shaping and re-shaping of calculations by guesswork. We draw on the interplay between guessing and computation in digital contexts that, perhaps surprisingly, include living organisms. Digital computation and communication depend on a type of synchronization that coordinates transitions among physically distinct conditions represented by “digits.” This logical synchronization, known to engineers but neglected in physics, requires guesswork for its maintenance. By abstracting digital hardware, we model the structure of human thinking as logically synchronized computation, punctuated by guesses.

    We adapt marked graphs to mathematically represent computation and represent guesses by unpredictable changes in these marked graphs. The marked graphs reveal a logical substructure to spatial and temporal navigation, with implications across physics and its biological applications. By limiting our model to the logical aspect of communications and computations—leaving out energy, weight, shape, etc.—we unveil logical structure in relation to guesswork, applicable not just to electronics but also to the functioning of living organisms.

    This idea, that guesses link the inner lives of investigators to their explanations is really intriguing. A guess is directed, limited, based on something — it is a sense of sorts. Guessing as a particular sense, like hearing, may be a fruitful path forward in exploring the nature of guesses as well. This may also explain what we could call the “unreasonable effectiveness” of guessing.

    The same authors also claimed to have proven that guesses are necessary in some kinds of scientific work — see the notes below. That is also quite intriguing!

    And of course, and this is the perhaps bigger thing here, this all leads us to the question of the role of abduction overall in science, thinking and reasoning. But that is a much longer discussion – and one for the future. The relationship between abduction and guessing is key here – and one we will come back to.

    Finally, a connection to my day to day work: can artificial intelligence help us guess better? And can an AI guess at all? What would it mean for it to guess, and how would you implement a guessing algorithm?

    The question of how you would design a guessing AI allows us to approach the question in a slightly different, design-oriented way. There is a wider point to this, the idea that maybe we need to be able to design something to understand it, that I think is undervalued in cognitive science, philosophy and psychology. The question of how to build a guessing computer forces us to think about how we would design the data, algorithms and interactions of that software in order to enable it to guess — and we also have to decide if we believe that there is a difference between guessing and predicting — or to put a point on it: do we believe that LLMs are really just large guessing engines? If we do – and we may well want to do that – we also realize that they are not information retrieval mechanisms; they are something very different — but not necessarily less valuable. The ability of an economy to collectively and collaboratively guess may well be key to its growth and expansions — and the role of guessing in the economy also becomes fascinating here!

    So much more to do and think about – let me know any thoughts in comments!

    Other papers I want to look more closely at:

    Allison, J., Riggs., Jack, E., Riggs. (2004). (2) “Guessing it right,” John A. Simpson, and myasthenia gravis: The role of analogy in science. Neurology, Available from: 10.1212/01.WNL.0000106936.27018.EC

    Mark, Tschaepe. (2013). (1) Gradations of Guessing: Preliminary Sketches and Suggestions. Contemporary Pragmatism, Available from: 10.1163/18758185-90000263

    Enric, Trillas. (2023). (5) A Discourse on Guessing and Reasoning. Lecture Notes in Computer Science, Available from: 10.1007/978-3-031-31476-6_14

    John, Myers., F., Hadi, Madjid. (2000). (11) A proof that measured data and equations of quantum mechanics can be linked only by guesswork. arXiv: Quantum Physics.

    Tadeg, Quillien., Neil, R, Bramley., Christopher, Lucas. (2023). (15) Guesses as compressed probability distributions. Available from: 10.31234/osf.io/gy2fv

    Footnotes and references

    • 1
      Povinelli, D.J., Nelson, K.E. and Boysen, S.T., 1990. Inferences about guessing and knowing by chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 104(3), p.203.
    • 2
      Myers, J.M. and Madrid, H., 2024. Synchronizing Rhythms of Logic. arXiv preprint arXiv:2405.20788.

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  • Notes on philosophy of science I: Facts without explanations

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    Here a few notes on a question that I think would be worthwhile to explore more in detail in an essay — these are just my first notes.

    In Narayanamurti’s and Tsao’s excellent book on how to nurture research – The genesis of technoscientific revolutions: rethinking the nature and nurture of research – the author suggest that science is composed of three elements – facts, explanations and generalizations. This model – admittedly simple – is helpful when we need to think about how science as a practice might change over time, especially with the introduction of artificial intelligence methods.

    Facts, they suggest, are observed patterns and regularities in nature, and explanations provide causal understanding of the facts and how they are connected in different ways. If this is the case we can ask an interesting question: can there be facts without explanations?

    It seems possible: we can observe a pattern or regularity without knowing why this regularity holds, but on the other hand the observation of a patterns seems to entail at least the possibility of finding an explanation for it — what if we used a new kind of scientific instrument that could detect patterns, but not provide us with any explanations for them, would those patterns then be facts?

    The opposite view – that patterns that cannot even in theory be explained can never be facts – suggests something interesting about the notion of facts. Facts are patterns, then, in this view, that can at least in theory be explained. (Something like this Let F be a fact, P be a pattern, and E be an explanation. ∀P(∃E(E explains P) → P is F))

    Some connection exists between this set of problems and the question of correlation and causation, and the challenge is made more complicated by the observation that as the number of variables in an observed set increases, the number of spurious correlations also increase. But what if some of those spurious correlations are stable enough to behave like facts? Is stability of a pattern a part of its “factiness”?

    And is there a way to explore if a pattern can, in theory, be explained? What if a pattern is n-dimensional, and n is so large that it is not possible for us to grasp an explanation with that many dimensions? Will a simplification be enough? And if it is not – are we then comfortable with connecting the notion of fact with our ability to understand an explanation?

    To some degree this is a question about the world we live in and the kind of knowledge we expect to find. Do we think there is scientific theories that are true, but inaccessible to us because of the complexity of those theories? If so, what is the size of the graspable set of scientific theories, and how does that relate to the whole?

    We may, of course, reject the entire idea that facts are patterns, and suggest that patterns are not enough to establish facts (I do like the notion of patterns as facts, if for no other reason that it allows us to factor in time into the notion of facts, and it allows for facts to change and even deteriorate (see Sam Arbesman!)).

    Much more to do here. A few thought experiments to consider:

    • The mysteriously stable correlation. Tyler Vigen has shown that per capita margarine consumption and divorce rate in Maine correlate, and provided some great notes on this here. Now, let’s assume we consciously increase the per capita consumption of margarine, and we see the divorce rate go up — and we then restrict the consumption and it goes back down, ever more precisely correlated. There seems to be no causal relationship – is this now a fact just because the correlation is stable AND allows for predictions based on it, or is it something else?
    • The black box predictor. A machine you can ask any question about a physical system and it answers it in ways you could not answer with today’s physics – but always correctly. Are the machine’s predictions facts?
    • The machine theory of everything. An AI assures us that there is a connection that allows for the famed theory of everything, but that it can’t explain it to use because the number of dimensions of the theory are too many and we lack concepts and language for it — but it finds 5 predictions that bear the assertion out in the sense that it predicts things we cannot predict today, and asserts this is because it bases those predictions of this n-dimensional theory. Is this theory scientific?

    And so on. Some of these thought experiments can be expanded and sharpened further — and I think this is not just idle speculation.

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  • Outcome scales and policy work – a note on craft

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    A key problem in public policy work is to articulate the case for intervening and for engaging in an issue – and this is mirrored by how challenging it is to actually report on and measure impact. A classical failure mode is the policy team working hard to get a proposal to a good place, succeeding, and no one realizing what the actual outcome would have been but for the intervention the team made – massively discounting the work.

    This ambiguity is natural – politics and policy are messy systems – but there are a few things we can do to improve.

    One important tool to add to your craft is the outcome scale. This is a list of outcomes from most impact (100) to least impact (0) of some kind of upcoming decision or process. Let’s say that we are looking at a new bill about the technology our company is developing. The bill is in early stages, maybe it has not even been formulated as a bill yet, but we do know that legislation is likely to emerge in the coming months — what should we then do to prepare, and ensure that we can share with leadership how we should be thinking about this?

    The answer is to list the possible outcomes from almost no effect to severe impact on the company’s optionality and viability. This list of outcomes is your scale – and what you will be managing.

    The different packages here – A to E – are collections of regulatory interventions and legislative restrictions you can predict are likely to be a part of the overall deal. The way you should think about this is that these packages represent the spectrum of the possible – we are not looking at probabilities at this stage – and so you need to be expansive. E – the maximum outcome – may be a ban on the technology, strict liability for any deployment et cetera, and once you have done a best effort ordering of the outcomes you use this to validate with the leadership of your business, and ensure that the business agrees with your assessment of impact, the bundling you will have done and the order of the bundles. Then the decision you should seek is what bundle to aim for.

    In order to make this decision, however, you need to add a piece of data for leadership to consider, and that is the cost it will take to get to the outcome in question. This is another assessment you need to make, and one way of doing that is to map all the stakeholders and try to imagine their relative power over the issue, and where they would prefer to land. This will give you a sense of the probability distribution over the different outcomes – and that can look very different for different issues. As you overlay the probability you can get something like this:

    What you then need to understand is if the probable outcome is within the acceptable limit, and if there is anything you can do to affect the overall probability distribution here. These curves aggregate all the different stakeholders, but you can also break them out to understand their preferences:

    Here we model 3 different stakeholders: A strongly prefers outcome B, C prefers outcome C or D, but is hesitant to go all to E, and B has a much weaker engagement overall over the different outcomes. This models both salience of the issue and preferences for the different stakeholders — and one analysis you will want to make at some point is how robust these preferences are (i.e. can you make B care much more about getting outcome C?).

    Finally, you also want to make it clear how much it will cost in time, resources and effort to get to the different outcomes. Here, again, issues have different profiles. Look at issue X and Y in these diagrams:

    Issue X is really costly to get out of outcome E, but it is almost equally costly to get to B and A. Note that this does not need to be the same as the probability — the probability that an issue ends up somewhere may be due to simple things, and there may be low-cost interventions that can shift the outcome. But when the cost looks like this, the probability is reasonably higher for an E outcome.

    Issue Y is different:

    Here the worst case outcomes is fairly easy to avoid — but it gets tricker getting from B to A.

    These very simple models serve two different purposes. First, they allow you to get to a shared view on the spectrum of outcomes (this avoids surprises), the desired outcome and the cost and probability of getting there. Modeling other stakeholders also gives you an engagement map (for more on stakeholder mapping see here).

    Techniques like these can also be used to predict the likely outcomes of different decisions. A robust version of this method can be found in the works of Bruce Bueno de Mesquita’s interesting book Predictioneer. He takes a model like this and adds game theory to model the likely outcomes — and this makes it technically harder and more demanding. The use of outcome scales I prefer is to help create this shared model of reality – and then prioritize work, and decide when to engage and how.

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  • How would you build a team of questioners?

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    Imagine a world in which your resume was the kinds of questions you are better at asking than most other people? If your skills were translated to the kinds of questions where you excel and really make a difference in a team? And if we then also decided to recruit on the basis of which new questions we need to ask in our different teams — how would that work?

    It seems clear that a team where everyone asks the same questions is likely to be less effective than a team where the members ask different questions, if for no other reason than the fact that the latter team will cover more information than the former. Different questions build a richer model of the world, and, to some degree this will help make better decisions. At some point the richness of the model may make it impossible to use as a model, but we are usually not even close to that in most corporate settings. The problem there, I think, is usually that we have models that are so simple – or even unarticulated – as to make decision making weak.

    So, imagine then that you want to put together a team of questioners – what would you be looking for? Here are a few ideas.

    • Fact-based questioners. People who ask after the facts, after data and relentless seek observations and empirical evidence. Their key question is “What is going on here?”
    • Process-questioners. These are folks who ask what questions we are not asking, what would change our minds, and similar things about the process.
    • Political questioners. Here we find the folks who understand organizational politics, figure out how needs to be in the loop, manage stakeholders in different ways.
    • Allocation questioners. To keep us honest when it comes to how we allocate attention, time and resources.
    • Holistic questioners. Folks who ask how what we do fits together with the larger picture, and with the wider mission of the organization.
    • Learning questioners. The idea here is that someone should always ask what we have learned during the process — this is a little like process questioners, but focused on how we compound our knowledge into actionable wisdom.

    These are just a few different ideas, you can surely come up with your own team set up here too — the team, then, can be viewed as running of a series of questions that they evolve, curate and build on in order to sketch out a provisional answer. You can also think about this as different roles if you want to, and try to build an ideal set of roles to fill:

    1. The Historian

    • Role: The historian brings context and depth to questions by linking them to historical events, trends, and figures. They ask questions like, “How has this issue evolved over time?” or “What can we learn from similar situations in the past?” – and their key questions are based in historical analogy.
    • Skills: Deep knowledge of history, ability to draw parallels across time periods, contextual analysis.

    2. The Philosopher

    • Role: The philosopher challenges assumptions and delves into the fundamental nature of concepts and ideas. They ask questions like, “What are the underlying principles at play?” or “Is this line of reasoning logically sound?” – their key questions are conceptual.
    • Skills: Critical thinking, ethical reasoning, expertise in philosophical traditions.

    3. The Scientist

    • Role: The scientist approaches questions with a methodical and evidence-based mindset. They ask questions like, “What empirical evidence supports this claim?” or “How can we test this hypothesis?” – their key questions are experimental.
    • Skills: Research methodology, data analysis, empirical reasoning.

    4. The Detective

    • Role: The detective is skilled at uncovering hidden details and exploring the depths of a situation. They ask questions like, “What details are missing?” or “Is there an alternative explanation?” – their key questions are abductive, looking for different explanations.
    • Skills: Investigative techniques, attention to detail, pattern recognition.

    5. The Journalist

    • Role: The journalist focuses on uncovering the truth and communicating it effectively. They ask questions like, “Who benefits from this information?” or “How can this be clearly explained to the public?” – their key questions are communicative.
    • Skills: Communication, investigative reporting, ethical journalism.

    6. The Analyst

    • Role: The analyst dissects information to understand its components and implications. They ask questions like, “What are the key drivers of this situation?” or “What are the potential outcomes?” – their key questions are model building.
    • Skills: Data analysis, logical reasoning, strategic thinking.

    7. The Ethnographer

    • Role: The ethnographer brings a cultural perspective, asking questions about the social and cultural contexts. They ask questions like, “How does this issue affect different communities?” or “What cultural factors are influencing this?” – their key questions are based in a focus on human elements.
    • Skills: Cultural analysis, qualitative research, empathy.

    8. The Technologist

    • Role: The technologist explores the impact of technology on the issue at hand. They ask questions like, “How does technology shape this problem?” or “What are the ethical implications of this technology?” – their key questions are actor-network theory questions.
    • Skills: Understanding of technology, digital ethics, systems thinking.

    9. The Legal Expert

    • Role: The legal expert examines the legal frameworks and implications of a situation. They ask questions like, “What laws are relevant to this issue?” or “What are the potential legal consequences?” – their key questions tend to be institutional.
    • Skills: Legal analysis, regulatory knowledge, rights advocacy.

    10. The Sociologist

    • Role: The sociologist looks at how social structures and relationships influence the issue. They ask questions like, “What are the societal implications?” or “How do social norms affect this situation?” – their key questions are normative.
    • Skills: Social theory, structural analysis, community engagement.

    11. The Creative Thinker

    • Role: The creative thinker challenges conventional wisdom and explores out-of-the-box ideas. They ask questions like, “What if we approached this differently?” or “How can we innovate here?” – their key questions are contrarian.
    • Skills: Lateral thinking, innovation, ideation.

    12. The Strategist

    • Role: The strategist focuses on long-term goals and plans. They ask questions like, “What is the big picture?” or “How does this align with our broader objectives?” – their key questions are asked against a different time horizon.
    • Skills: Strategic planning, foresight, risk assessment.

    Now, you may say that this is a complicated and labored way of thinking about teams, and I would – perhaps surprisingly – agree with you. But the main point here is not to say that this way of composing teams is ideal, but rather to draw your attention to the fact that this is how we compose teams today, but with answering skills. Skills are, usually, cast in terms of the kinds of questions you can answer, rather than the kinds of questions that you are very skilled at asking. And that is as silly as just focusing on the questions — so maybe the overall insight here is that we need a balance, and we need to make sure that we understand how people ask questions too.

    I have made it a practice in interviews to leave some space for the interviewee to ask questions. Most of my hiring decisions, I think, come from the kinds of questions people ask me in this part of the interview – since the questions are so much more revealing than the kinds of answers you get in a job interview. We all prep for job interview questions and know that we should have answers to questions like “why do you want to work here?” – even if that is a particularly bad question in itself. You can use it to understand if people have indeed prepped, because this question will then be answered like someone reciting the answer to a test question — with main points prepared and launched at you. But opening it up for questions is different — where someone has prepared real questions the interview suddenly becomes a dialogue, and you can explore how people think in a very different way.

    The perhaps most problematic reply you can get is that the interviewee has no questions at all. Sometimes this is out of respect for your time, and you may need to stress that you have time — but sometimes it is because the person interviewing genuinely does not have any questions, and that is surprising to me. You want to work at this company, and you have no questions about it?

    Some of the most effective people I know are defined by their questions. They use questions to prioritize their work, they ask more questions in a 1.1 than lecture, and they think through the kinds of question map you are likely to face in a complex project: when you answer one question, there will be others, and look to address those dependencies effectively. They have stock questions like “what does a good outcome look like here?” and “what happens if we do nothing?” — and often use these to make sense of complexity, overload and noise problems.

    So maybe we would do well to explore what kinds of questions a new team member will bring to the table?

    And if we use this idea for some self-reflection – which questions are you better at asking than many other people? Are there questions you often ask in a group that would otherwise not be asked? What are your top ten questions for work? Your most undervalued questions? The questions you dislike the most? Adding a small component to your resume that is titled “My questions” would surely signal at least the curiosity that we all look for in a candidate?

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