Articles tagged with games

1. Who makes the decisions?

   When the internet first came along, it immediately transformed intelligence collection and analysis. Suddenly, there was a new source of material, unfiltered through governments and newspapers. Over the years the flood gates opened wider and wider, until the amount of material became unmanageable by traditional analysis techniques.

   Big data is a loaded term. It means something different to a Google or Facebook who analyse their customers in a strictly limited domain, trying to prove click attribution or ranking search results. Statistical methods are effective ways of doing this.

   In intelligence, there’s a hope that we can use AI to make sense of the deluge of information. But we must be careful. Our best AIs today will give you a facsimile of a person from 2024, but past performance is not indicative of future results. After all, an AI trained on data up to and including the 18th century will give you a facsimile of an 18th century person, but won’t tell you anything about what happens in the 19th.

   Perhaps the biggest problem we face is a poverty of imagination. We have fallen into narrative-based thinking, modelling adversaries and allies as predictable economically-driven automatons, even as we’re hit by events that we failed to predict, or often were told were impossible — everything from Trump and Brexit to Ukraine and Gaza. There’s a real danger that a naive approach to integrating AI decision support will weaken our ability to understand reality by constructing a fantasy cocoon of statistically based reasoning about the world.

   All is not lost. We can take advantage of AI to help us with the flood of source material, but not simply by training models on the data and expecting those models to spit out predictions. Instead, we must look to the work done on frameworks intended to make sense of human behaviour for human analysts, frameworks which are used today to great effect in the field and in training.

   At Deep Drama, one approach we use is to use LLMs to automate the generation of Confrontation Analysis (Drama Theory) models from intelligence sources. With those models as a base, other LLMs can provide insights, simulations and decision support tied to a causal chain of reasoning from the model, not from the blackbox AI.

   The goal of conflict analysis must be to understand our adversaries. In a complex, multi-polar world, this is a daunting task, and we can and should use AI to help. However, real help does not come as predictions from a blackbox. At Deep Drama we are building “AI intelligence analysts” that can sit alongside decision makers, keeping track of the complexity and providing guard rails for decisions as they are made.

2. Against prediction

   Everybody wants someone to tell them what's going to happen. But whatever consciousness is, it's fundamentally unpredictable. As long as you're in the human domain, telling the future is out.

   There's a story Drama Theorists use to illustrate this. A husband and wife are playing chess. The husband makes a move, "checkmate". The wife objects, wait, there must be something I can do. No, says the husband, it's checkmate -- there are no possible moves. The wife reaches out and smashes the board into the air, pieces flying, "how about this for a move?".

   Okay, so life isn't a game, there aren't rules. So what then? If analysis isn't predictive, what is it? Analysis is the art of understanding the present moment as fully as possible. Choosing models for compressing reality to aid decision making. The actions that make up the future will come from unpredictable human creativity. A wider understanding of the present moment gives our creativity the best grounding from which to imagine the future.

   AI can't predict the future, but it can help us model the present.

3. Decision making needs explainable AI

   Decision making is being transformed by AI technology. LLMs are a new capability, allowing us to quantitatively model human behaviour like never before. However, using LLMs to drive a statistical approach to decision making will only end in disillusionment.

   Those who invent a technology often are not best placed to take advantage of that technology. The current advances in AI were born from statistics, and it's a natural impulse to use them for “statistical” approaches. For example, one might try to use the ability to mine vastly larger datasets to predict human behaviour from analysis of past behaviour. This statistical black box approach is wrong. Human behaviour is inherently unpredictable. Decision making is about choices, not narratives.

   Instead, we can use LLMs to supercharge our modelling of human behaviour. The models are explainable because they are using frameworks intended for human use. LLMs can help humans to understand, while the explainable model is always there as a ground-truth. We can also use generative AI to make exploratory sallies into possible near-futures. All of this gives decision makers better understanding of their choices and those of the counter-parties.

   In decision making, there is no black box that will output a perfect strategy. Better choices come from a more complete understanding of the situation. We must be careful never to fool ourselves with statistically derived far-reaching narratives. In the end, our opponents are creative humans, not trendlines on a graph.

4. Anatomy of a Fall

   Over the weekend, I saw the movie Anatomy of a Fall. It was fascinating to get a look at the French justice system. Common Law justice systems seem to be highly overrepresented in film. I'm not sure whether this is because the British and Americans have a particular liking for courtroom drama, or I'm just not watching the right foreign movies.

   The question at the heart of the movie was, did Sandra kill her husband? You might think this is the model:

   	Sandra
   Sandra
   Kill Samuel	?

   However, this isn't quite right, as the Option "Kill Samuel" doesn't refer to a future possibility. Rather, the drama in the film revolves around whether Sandra will be convicted for murder:

   	Sandra	Prosecutor	dilemmas
   Court
   Convict Sandra	⨉	✓	Sandra, Prosecutor

   The Court's position is of course unstated. This means that Sandra (and by extension her Defence) and the Prosector both have persuasion dilemmas with respect to the Court. They are compelled to persuade the Court to take their position.

   Much of the drama in the film comes from how powerless Sandra is to influence events. She provides compelling testimony, but so does the Prosecutor. In the end, it comes down to her son Daniel's testimony, and whether he will take her side.

   	Sandra	Prosecutor	Daniel	dilemmas
   Court
   Convict Sandra	⨉	✓	-	Sandra, Prosecutor
   Daniel
   Testify against Sandra	⨉	✓	-	Sandra, Prosecutor

   This gives Sandra and the Prosecutor persuasion dilemmas with respect to Daniel. Sandra in particular is compelled to persuade him to reject the idea of testifying against her. This is fraught, as if Daniel takes his Mother's side, then it means accepting the idea that his father took his own life. For much of the film, Daniel is clearly undecided as to whether he believes his Mother (represented by the -). The film's climax comes when Daniel makes his decision over this option. Although the film does not take a side, ultimately it seems that Daniel persuades himself of his Mother's innocence.

   Actually, when we lack an element to judge something, and the lack is unbearable, all we can do is decide. You see? To overcome doubt, sometimes we have to… decide to sway one way rather than the other. Since you need to believe one thing but have two choices, you must choose.

   	Sandra	Prosecutor	Daniel	dilemmas
   Court
   Convict Sandra	⨉	✓	⨉	Sandra, Prosecutor, Daniel
   Daniel
   Testify against Sandra	⨉	✓	⨉	Sandra, Prosecutor