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70		<p class="crumbs"><a href="/">Home</a> <span class="sep">/</span> <a href="/blog/">Blog</a></p>
71
72		<p class="post-kicker">Careers</p>
73
74		<h1 class="post-title">Optimism in the face of uncertainty</h1>
75
76		<p class="post-standfirst">What a principle from reinforcement learning can teach us about
77		research, careers, and taking intelligent risks.</p>
78
79		<p class="post-byline">
80			<b>Chen-Yang Su</b>
81			<span class="dot">&middot;</span>
82			<time datetime="2026-08-30">30 August 2026</time>
83			<span class="dot">&middot;</span>
84			<span data-reading-time>&nbsp;</span>
85		</p>
86
87		<div class="takeaways">
88			<span class="takeaways-label">Main takeaways</span>
89			<ul>
90				<li>The phrase is not a slogan. It comes from bandit algorithms such as
91				<strong>Upper Confidence Bound</strong>, where an action becomes attractive not
92				because its estimated reward is high, but because the uncertainty around that
93				estimate leaves room for it to be much better.</li>
94				<li>Optimism here does not mean believing things will work out. It means treating
95				<strong>uncertainty as something with value</strong>, because an experiment that
96				fails still returns information.</li>
97				<li>Scoring a decision by its single outcome confuses the <strong>quality of the
98				decision</strong> with the result of one trial.</li>
99				<li>The bets worth taking share three properties: <strong>survivable downside,
100				meaningful upside, and something learned either way</strong>. That rules out
101				recklessness as firmly as it rules out timidity.</li>
102				<li>Jo&euml;lle Pineau calls a degree a <strong>licence to take risks</strong>, and
103				makes the same argument about open science one level up: you give up some control
104				over what happens to your work, and far more people can build on it.</li>
105			</ul>
106		</div>
107
108		<div class="prose">
109
110			<p>In June 2026, my MSc advisor,
111			<a href="https://www.mcgill.ca/qls/researchers/joelle-pineau" target="_blank" rel="noopener">Jo&euml;lle Pineau</a>,
112			received an
113			<a href="https://www.uottawa.ca/faculty-education/news-all/uottawa-confer-nine-honorary-doctorates-spring-convocation" target="_blank" rel="noopener">honorary doctorate from the University of Ottawa</a>
114			and delivered the commencement address to the graduating class of its Faculty of
115			Engineering. Jo&euml;lle is a professor at McGill, previously led Meta&rsquo;s Fundamental
116			AI Research organization, and is now Chief AI Officer at Cohere.</p>
117
118			<p>Her address had a title that immediately caught my attention:
119			<a href="https://www.linkedin.com/pulse/optimism-face-uncertainty-joelle-pineau-r2c7e" target="_blank" rel="noopener"><b>&ldquo;Optimism in the Face of Uncertainty.&rdquo;</b></a></p>
120
121			<p>It sounds like a piece of general life advice. But the idea came from somewhere much
122			more precise: mathematics.</p>
123
124			<p>Jo&euml;lle described a principle she encountered while studying how intelligent
125			agents should make decisions when they do not know exactly what will happen next.</p>
126
127			<p>When faced with several possible actions, the obvious strategy is to choose the one
128			that currently looks best.</p>
129
130			<p>But that is not always the best strategy.</p>
131
132			<p>Sometimes the most interesting action is the one whose outcome is uncertain, but whose
133			<strong>upside could be much larger</strong>.</p>
134
135			<p>This principle is known as <strong>optimism in the face of uncertainty</strong>.</p>
136
137			<p>And the more I thought about it, the less it seemed like a lesson about reinfor
137cement
138			learning and the more it seemed like a useful way to think about research, careers, and
139			life.</p>
140
141			<h2>A mathematical reason to be optimistic</h2>
142
143			<p>Imagine that an agent has several actions available to it.</p>
144
145			<p>One option has been tried many times. Its reward is reasonably good and reasonably
146			predictable.</p>
147
148			<p>Another has barely been explored. Its average estimated reward might look lower, but
149			because the agent knows so little about it, there is considerable uncertainty around that
150			estimate.</p>
151
152			<p>A purely greedy agent chooses the first option.</p>
153
154			<p>An optimistic agent asks a different question:</p>
155
156			<p><strong>How good could this unexplored option plausibly be?</strong></p>
157
158			<p>This basic idea appears throughout reinforcement learning and the study of multi-armed
159			bandits. Algorithms such as <strong>Upper Confidence Bound (UCB)</strong> deliberately
160			account for uncertainty when deciding what to explore. An action can become attractive not
161			merely because its estimated reward is high, but because there remains a plausible chance
162			that it is substantially better than we currently believe.</p>
163
164			<div class="callout callout-note">
165				<span class="callout-label">The principle</span>
166				<p>Do not rank actions by their estimated reward. Rank them by the <b>upper end of
167				what they could plausibly be worth</b>, given how little you know. An action you have
168				barely tried carries a wide confidence interval, and the top of that interval is a
169				reason to try it again.</p>
170			</div>
171
172			<p>There is an elegant logic behind this.</p>
173
174			<p>If the optimistic estimate turns out to be right, you discover something valuable.</p>
175
176			<p>If it turns out to be wrong, you still gain information.</p>
177
178			<p>As Jo&euml;lle put it in her commencement address, when reality falls short of your
179			optimism, you have not necessarily lost: <strong>you have gained knowledge.</strong></p>
180
181			<p>That is a subtle but important distinction.</p>
182
183			<p>Optimism here does not mean convincing yourself that everything will work out.</p>
184
185			<p>It means recognizing that <strong>uncertainty can have value</strong>.</p>
186
187			<h2>The same problem appears in our careers</h2>
188
189			<p>The connection to life becomes especially clear around graduation.</p>
190
191			<p>Suddenly, there is an enormous decision tree in front of you.</p>
192
193			<p>Which job should you take? Which city should you move to? Should you stay in academia?
194			Join industry? Start something? Work on the established problem where you know you can
195			succeed, or the strange new question where you have no idea what will happen?</p>
196
197			<p>We often respond to these decisions by trying to eliminate uncertainty.</p>
198
199			<p>We gather more information. Make spreadsheets. Compare salaries. Ask more people for
200			advice. Try to predict exactly where each path will lead.</p>
201
202			<p>Some of this is obviously sensible.</p>
203
204			<p>But eventually there is a limit to how much more thinking can tell you.</p>
205
206			<p>At that point, uncertainty is not something that can be reasoned away. It has to be
207			<strong>experienced</strong>.</p>
208
209			<p>You have to choose an action and observe what happens.</p>
210
211			<p>That is why one of my favourite lines from Jo&euml;lle&rsquo;s address was her
212			description of a degree as a <strong>&ldquo;license to take risks.&rdquo;</strong> She
213			encouraged the graduates to say yes to the project that seems too ambitious, the role they
214			do not quite feel ready for, or the question nobody in their field has thought to ask.</p>
215
216			<p>I think that is particularly good advice early in a career.</p>
217
218			<p>When you are young, the benefits of exploration have a long time to compound.</p>
219
220			<p>A project that goes nowhere might introduce you to an entirely new field.</p>
221
222			<p>A job that turns out not to fit might clarify what kind of work you actually want to
223			do.</p>
224
225			<p>An unsuccessful application might lead you to meet someone you otherwise never would
226			have spoken to.</p>
227
228			<p>A research question that fails might reveal a much better question hiding underneath
229			it.</p>
230
231			<p>The outcome itself is only part of the return.</p>
232
233			<p>The other part is what the experiment teaches you.</p>
234
235			<h2>Optimize for information gain</h2>
236
237			<p>We tend to evaluate past decisions with an overly simple scoring system.</p>
238
239			<p>The paper was accepted: good decision.</p>
240
241			<p>The paper was rejected: bad decision.</p>
242
243			<p>The startup succeeded: good decision.</p>
244
245			<p>The startup failed: bad decision.</p>
246
247			<p>I got the position: good decision.</p>
248
249			<p>I didn&rsquo;t get it: bad decision.</p>
250
251			<p>
251But that confuses the <strong>quality of a decision</strong> with the
252			<strong>outcome of a single trial</strong>.</p>
253
254			<p>Suppose you spend three months exploring an ambitious research idea and eventually
255			conclude that it does not work.</p>
256
257			<p>You could call those three months wasted.</p>
258
259			<p>But perhaps during those three months you learned a new method, became familiar with a
260			literature you had never encountered, met several collaborators, discovered why an
261			apparently promising idea fails, and stumbled across a different problem that turns out to
262			be much more interesting.</p>
263
264			<p>The experiment failed.</p>
265
266			<p>The information gain did not.</p>
267
268			<p>That suggests a question worth adding whenever we face an uncertain opportunity:</p>
269
270			<p><strong>What will I know after doing this that I don&rsquo;t know now?</strong></p>
271
272			<p>This changes how many opportunities look.</p>
273
274			<p>Something with only a moderate probability of immediate success may still be an
275			excellent bet if:</p>
276
277			<ul>
278				<li>the downside is limited,</li>
279				<li>the potential upside is large, and</li>
280				<li>failure will teach you something useful.</li>
281			</ul>
282
283			<p>That is a very different criterion from simply choosing the path most likely to
284			succeed.</p>
285
286			<h2>Run cheap experiments</h2>
287
288			<p>There is, however, an important caveat.</p>
289
290			<div class="callout callout-warn">
291				<span class="callout-label">Optimism is not recklessness</span>
292				<p>&ldquo;Optimism in the face of uncertainty&rdquo; should not become an intellectual
293				justification for recklessness. Some decisions have irreversible consequences. Some
294				risks have enormous downside. <b>The fact that an outcome is uncertain does not
295				automatically make it worth exploring.</b></p>
296			</div>
297
298			<p>The most attractive experiments are often the opposite: <strong>small downside, large
299			upside, fast feedback</strong>.</p>
300
301			<ul>
302				<li>Send an email to someone whose work you admire.</li>
303				<li>Apply for the position you think you are slightly underqualified for.</li>
304				<li>Spend a weekend building the prototype.</li>
305				<li>Try the new research direction for two weeks.</li>
306				<li>Submit the paper.</li>
307				<li>Take the difficult course.</li>
308				<li>Write the essay.</li>
309				<li>Go to the conference.</li>
310				<li>Ask the question.</li>
311			</ul>
312
313			<p>These actions can fail.</p>
314
315			<p>But most failures are cheap.</p>
316
317			<p>And occasionally one of them changes the trajectory of your life.</p>
318
319			<p>Perhaps the better rule, then, is not simply <i>take more risks</i>.</p>
320
321			<div class="callout callout-good">
322				<span class="callout-label">The rule</span>
323				<p><strong>Seek uncertainty when the downside is survivable, the upside is meaningful,
324				and the outcome will teach you something.</strong></p>
325				<p>The goal is not to maximize the number of risks you take. It is to run
326				<b>better experiments</b>.</p>
327			</div>
328
329			<h2>There is an asymmetry to opportunity</h2>
330
331			<p>Another reason this framing is useful is that many interesting opportunities have
332			asymmetric payoffs.</p>
333
334			<p>Imagine emailing a researcher you admire.</p>
335
336			<p>Maybe there is a 90% chance nothing happens.</p>
337
338			<p>That sounds like a terrible bet if you evaluate it by probability of success alone.</p>
339
340			<p>But suppose the cost of sending the email is ten minutes, while the 10% outcome could
341			produce a collaboration, mentorship, job opportunity, or entirely new research direction.</p>
342
343			<p>The expected value begins to look very different.</p>
344
345			<p>The same principle applies to applications, introductions, side projects, conferences,
346			writing publicly, and ambitious research questions.</p>
347
348			<p>Humans often overweight the probability of embarrassment or rejection and underweight
349			the magnitude of the upside.</p>
350
351			<p>We see the likely outcome:</p>
352
353			<p><strong>They probably won&rsquo;t respond.</strong></p>
354
355			<p>We pay much less attention to:</p>
356
357			<p><strong>But what if they do?</strong></p>
358
359			<p>Optimism in the face of uncertainty forces us to consider both.</p>
360
361			<h2>Research itself depends on this mindset</h2>
362
363			<p>The idea feels particularly natural in science.</p>
364
365			<p>Good research begins with uncertainty.</p>
366
367			<p>If we already knew the answer, there would be little reason to run the experiment.</p>
368
369			<p>Yet researchers can gradually become surprisingly good at avoiding uncertainty.</p>
370
371			<p>We learn what reviewers like.</p>
372
373			<p>
373We learn which analyses are predictable.</p>
374
375			<p>We learn which questions can produce another incremental publication.</p>
376
377			<p>And those incentives can push us toward exploitation rather than exploration:
378			repeatedly mining areas where we already know rewards are available.</p>
379
380			<p>But some of the most interesting research exists precisely where the confidence
381			intervals are widest.</p>
382
383			<p>A strange result.</p>
384
385			<p>A new dataset.</p>
386
387			<p>A methodology borrowed from another discipline.</p>
388
389			<p>A question that sounds slightly ridiculous when you first say it aloud.</p>
390
391			<p>Most of these directions will not produce breakthroughs.</p>
392
393			<p>That is the point.</p>
394
395			<p>Exploration is valuable because you cannot know beforehand which one will.</p>
396
397			<h2>Open science as an optimistic bet</h2>
398
399			<p>Jo&euml;lle also connected this philosophy to another theme that has defined much of her
400			career: <strong>open science</strong>.</p>
401
402			<p>For years, she has pushed machine learning toward greater reproducibility and more
403			transparent research practices. Her work helped establish reproducibility initiatives at
404			NeurIPS, including code submission practices, reproducibility challenges, and checklists
405			for reporting machine-learning experiments. In a 2021
406			<a href="https://www.jmlr.org/papers/v22/20-303.html" target="_blank" rel="noopener"><i>Journal of Machine Learning Research</i> paper</a>,
407			she and her co-authors argued that reproducibility helps researchers verify findings,
408			build on one another&rsquo;s work, and move ideas into practice more reliably.</p>
409
410			<p>There is an element of optimism here too.</p>
411
412			<p>When you release code, data, models, or ideas openly, you surrender some control over
413			what happens next.</p>
414
415			<p>Someone may find a mistake.</p>
416
417			<p>Someone may use your work differently than you intended.</p>
418
419			<p>Someone you have never met may take your idea somewhere you never anticipated.</p>
420
421			<p>There is uncertainty.</p>
422
423			<p>But there is also enormous upside.</p>
424
425			<p>Thousands of researchers can inspect, reproduce, modify, and extend something that
426			would otherwise remain confined to a single laboratory.</p>
427
428			<p>In her commencement address, Jo&euml;lle described open source in essentially these
429			terms: sharing carries risks, but inviting more people to participate dramatically expands
430			what can be created.</p>
431
432			<p>The philosophy is consistent.</p>
433
434			<p>You accept some uncertainty because the space of possible positive outcomes becomes
435			much larger.</p>
436
437			<h2>The future cannot be optimized in advance</h2>
438
439			<p>There is a temptation, especially among ambitious people, to treat life as an
440			optimization problem.</p>
441
442			<p>Find the optimal degree.</p>
443
444			<p>The optimal lab.</p>
445
446			<p>The optimal company.</p>
447
448			<p>The optimal city.</p>
449
450			<p>The optimal career.</p>
451
452			<p>Collect enough information and, theoretically, the correct path will reveal itself.</p>
453
454			<p>But life is not a static optimization problem.</p>
455
456			<p>The environment changes.</p>
457
458			<p>You change.</p>
459
460			<p>New technologies appear.</p>
461
462			<p>People enter your life.</p>
463
464			<p>Interests you thought were permanent disappear.</p>
465
466			<p>Opportunities emerge that you could not possibly have anticipated five years
467			earlier.</p>
468
469			<p>There is no dataset containing enough information to calculate your optimal
470			trajectory.</p>
471
472			<p>At some point, you have to explore.</p>
473
474			<p>That may be the part of Jo&euml;lle&rsquo;s speech that resonates most with me.</p>
475
476			<p>Optimism does not require believing that every choice will succeed.</p>
477
478			<p>In fact, you should expect many of your experiments not to.</p>
479
480			<p>Papers will be rejected.</p>
481
482			<p>People won&rsquo;t reply.</p>
483
484			<p>Projects will stall.</p>
485
486			<p>Applications will fail.</p>
487
488			<p>Predictions you made confidently about your future will turn out to be completely
489			wrong.</p>
490
491			<p>The optimistic belief is something different:</p>
492
493			<p><strong>that acting is still worthwhile because action generates information.</strong></p>
494
495			<p>You try something.</p>
496
497			<p>The world responds.</p>
498
499			<p>You update.</p>
500
501			<p>And then you try again.</p>
502
503			<h2>Choose again</h2>
504
505			<p>When faced with uncertainty, our instinct is often to wait until we feel more
506			certain.</p>
507
508			<p>Sometimes that is appropriate.</p>
509
510			<p>But often the information we need is located on the other side of action.</p>
511
512			<div class="callout">
513				<span class="callout-label">Two better questions</span>
514				<p>Instead of asking <i>which choice guarantees success?</i>, ask
515				<b>which choice has the most interesting upside?</b></p>
516				<p>Instead of asking <i>what if this fails?</i>, ask
517				<b>what will I learn if it does?</b></p>
518			</div>
519
520			<p>Instead of trying to map your entire career from where you stand today, run the next
521			useful experiment.</p>
522
523			<p>Choose an action.</p>
524
525			<p>Observe.</p>
526
527			<p>Update.</p>
528
529			<p>Choose again.</p>
530
531			<p>The future is uncertain by definition.</p>
532
533			<p>That is not necessarily a problem to solve.</p>
534
535			<p>Sometimes it is the reason to act.</p>
536
537			<h2>Further reading</h2>
538
539			<ul>
540				<li><a href="https://www.linkedin.com/pulse/optimism-face-uncertainty-joelle-pineau-r2c7e" target="_blank" rel="noopener">Jo&euml;lle Pineau, &ldquo;Optimism in the Face of Uncertainty&rdquo;</a> &mdash; the commencement address this post is about.</li>
541				<li><a href="https://www.uottawa.ca/about-us/president/honorary-doctorates" target="_blank" rel="noopener">University of Ottawa, honorary doctorate recipients</a></li>
542				<li><a href="https://www.jmlr.org/papers/v22/20-303.html" target="_blank" rel="noopener">Pineau et al., &ldquo;Improving Reproducibility in Machine Learning Research&rdquo;</a>, <i>JMLR</i> 22(164), 2021.</li>
543				<li><a href="https://www.cs.mcgill.ca/~jpineau/" target="_blank" rel="noopener">Jo&euml;lle Pineau&rsquo;s website</a>, and her <a href="https://www.mcgill.ca/qls/researchers/joelle-pineau" target="_blank" rel="noopener">research profile at McGill</a></li>
544			</ul>
545
546		</div><!-- /.prose -->
547
548		<div class="post-foot">
549			<div class="tags">
550				<span class="tag">reinforcement learning</span>
551				<span class="tag">exploration</span>
552				<span class="tag">research careers</span>
553				<span class="tag">open science</span>
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