 The problem of induction is closely linked to the issue of simplicity. One obvious link is between the curve-fitting problem and the inductive problem of predicting future outcomes from observed data. Less obviously, Schulte (1999) argues for a connection between induction and ontological parsimony. Schulte frames the problem of induction in information-theoretic terms: given a data-stream of observations of non-unicorns (for example), what general conclusion should be drawn? He argues for two constraints on potential rules. First, the rule should converge on the truth in the long run (so if no unicorns exist then it should yield this conclusion). Second, the rule should minimize the maximum number of changes of hypothesis, given different possible future observations. Schulte argues that the ‘Occam Rule’—conjecture that \(\Omega\) does not exist until it has been detected in an experiment—is optimal relative to these constraints. An alternative rule—for example, conjecturing that \(\Omega\) exists until 1 million negative results have been obtained—may result in two changes of hypothesis if, say, \(\Omega\)’s are not detected until the 2 millionth experiment. The Occam Rule leads to at most one change of hypothesis (when an \(\Omega\) is first detected). (See also Kelly 2004, 2007.) Schulte (2008) applies this approach to the problem of discovering conservation laws in particle physics. The analysis has been criticized by Fitzpatrick (2013), who raises doubts about why long-run convergence to the truth should matter when it comes to predicting the outcome of the very next experiment, and the debate is continued in Kelly et al. (2016).