I’ll now do this for a particular toy example: the decision making problem of a soccer playing agent that tries to score a goal, with a human goalkeeper trying to block the goal. I simplify this toy world by looking at one particular case only: the case where the agent is close to the goal, and must decide whether to kick the ball in the left or right corner. As the agent is close, the human goalkeeper will have to decide to run to the left corner or right corner of the goal even before the agent takes the shot: the goalkeeper does not have enough time to first observe where the ball is going and only then start moving. So this toy world decision problem has the agent deciding on kick left of right, and the goalkeeper simultaneously deciding on running left or right.
By this setting, you ensure that the goal-keeper isn’t a causal descendant of the LCDT-agent. Which means there is no cutting involved, and the prior doesn’t play any role. In this case the LCDT agent decides exactly like a CDT agent, based on its model of what the goal-keeper will do.
If the goal-keeper’s decision depends on his knowledge about the agent’s predisposition, then what you describe might actually happen. But I hardly see that as a deception: it completely reveals what the LCDT-agent “wants” instead of hiding it.
By this setting, you ensure that the goal-keeper isn’t a causal descendant of the LCDT-agent.
Oops! You are right, there is no cutting involved to create C from B in my toy example. Did not realise that. Next time, I need to draw these models on paper before I post, not just in my head.
C and B do work as examples to explore what one might count as deception or non-deception. But my discussion of a random prior above makes sense only if you first extend B to a multi-step model, where the knowledge of the goal keeper explicitly depends on earlier agent actions.
By this setting, you ensure that the goal-keeper isn’t a causal descendant of the LCDT-agent. Which means there is no cutting involved, and the prior doesn’t play any role. In this case the LCDT agent decides exactly like a CDT agent, based on its model of what the goal-keeper will do.
If the goal-keeper’s decision depends on his knowledge about the agent’s predisposition, then what you describe might actually happen. But I hardly see that as a deception: it completely reveals what the LCDT-agent “wants” instead of hiding it.
Oops! You are right, there is no cutting involved to create C from B in my toy example. Did not realise that. Next time, I need to draw these models on paper before I post, not just in my head.
C and B do work as examples to explore what one might count as deception or non-deception. But my discussion of a random prior above makes sense only if you first extend B to a multi-step model, where the knowledge of the goal keeper explicitly depends on earlier agent actions.