{'fraction_non_alphanumeric': 0.021966527196652718, 'fraction_numerical': 0.0, 'mean_word_length': 5.6, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimization and Its Limits

Innovation dilemmas often result from seeking optimal—that is, minimal or maximal—outcomes. However, uncertainty sometimes makes outcome optimization infeasible or unwise. Human progress originates in our thirst for improvement. However, our ability to predict the outcome quality of the options is limited when our knowledge is severely curtailed. We simply can’t know which option will be optimal. Furthermore, seeking the best outcome sometimes becomes a moral imperative of its own, regardless of substantive needs. Optimization is then a goal in itself, leading to misuse of an otherwise worthy idea. We begin by discussing the modern paradigm of optimization—the laws of physics—and then discuss three ways in which seeking optimal solutions may go astray. After distinguishing between substantive and procedural optimization, we will see that outcome optimizers inadvertently minimize the robustness against uncertainty.