// example of extreme value
var extremeValues = {burden: 50,
                    prob: .00001,
                    quanta: 1500000}

var options = {method: 'forward', samples: 25000}

// The naive standard for reasonableness
var handPolicy = function(burden, prob, quanta) {
  return (burden < (prob * quanta) ?
          'take precaution' :
          'do what you want' );
}

// Might expect unreasonable people to just act randomly
// TODO: might add prior here (i.e. more likely to do what you want a priori)
// or replace with some other assumption, e.g. acting maliciously?
var randomPolicy = function(burn, prob, quanta) {
  return flip() ? 'take precaution' : 'do what you want';
}

// Generative model of an agent's behavior given their reasonableness,
// the facts of the situation, and the rule they're using to make a decision
var agent = function(reasonable, values, rule) {
  return Infer(options, function() {
    var burden = gaussian(values.burden, 45) // very uncertain about cost of burden
    var prob = beta(1, 0.1) // probability of harm
    var quanta = 100 // harm is 100
    return (reasonable ?
            rule(burden, prob, quanta) : // hand formula
            randomPolicy(burden, prob, quanta))
  })
}

// Jury sees defendent take action and uses some information about the situation
// and a rule for what constitutes reasonableness to make their judgement
var factFinder = function(actualAction, values, rule) {
  return Infer({method: 'enumerate'}, function() {
    // Jury's job is to infer whether defendent acted reasonably
    var reasonableness = flip();
    // Run a generative model of reasonableness (or not) under some rule
    var expectedAction = agent(reasonableness, values, rule)
    // Condition on the actual action coming from this distribution
    observe(expectedAction, actualAction);
    // Return their reasonableness
    return reasonableness;
  })
}

viz(factFinder('do what you want', extremeValues, handPolicy))
viz(factFinder('take precaution', extremeValues, handPolicy))