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Sunrise problem
Sunrise problem
Sunrise problem
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Usually inferred from repeated observations: "The sun always rises in the east".

The sunrise problem can be expressed as follows: "What is the probability that the sun will rise tomorrow?" The sunrise problem illustrates the difficulty of using probability theory when evaluating the plausibility of statements or beliefs.

According to the Bayesian interpretation of probability, probability theory can be used to evaluate the plausibility of the statement, "The sun will rise tomorrow."

The sunrise problem was first introduced publicly in 1763 by Richard Price in his famous coverage of Thomas Bayes' foundational work in Bayesianism.[1]

Laplace's approach

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Pierre-Simon Laplace approached the problem by means of his rule of succession.[2][3] Let p be the long-run frequency of sunrises, i.e., the sun rises on 100 × p% of days. Prior to knowing of any sunrises, one is completely ignorant of the value of p. Laplace represented this prior ignorance by means of a uniform probability distribution on p.

For instance, the probability that p is between 20% and 50% is just 30%. This must not be interpreted to mean that in 30% of all cases, p is between 20% and 50%. Rather, it means that one's state of knowledge (or ignorance) justifies one in being 30% sure that the sun rises between 20% of the time and 50% of the time. Given the value of p, and no other information relevant to the question of whether the sun will rise tomorrow, the probability that the sun will rise tomorrow is p. But we are not "given the value of p". What we are given is the observed data: the sun has risen every day on record. Laplace inferred the number of days by saying that the universe was created about 6000 years ago, based on a young-earth creationist reading of the Bible.

To find the conditional probability distribution of p given the data, one uses Bayes' theorem, which some call the Bayes–Laplace rule. Having found the conditional probability distribution of p given the data, one may then calculate the conditional probability, given the data, that the sun will rise tomorrow. That conditional probability is given by the rule of succession. The plausibility that the sun will rise tomorrow increases with the number of days on which the sun has risen so far. Specifically, assuming p has an a-priori distribution that is uniform over the interval [0,1], and that, given the value of p, the sun independently rises each day with probability p, the desired conditional probability is:

By this formula, if one has observed the sun rising 10000 times previously, the probability it rises the next day is . Expressed as a percentage, this is approximately a chance.

However, Laplace recognized this to be a misapplication of the rule of succession through not taking into account all the prior information available immediately after deriving the result:

But this number [the probability of the sun coming up tomorrow] is far greater for him who, seeing in the totality of phenomena the principle regulating the days and seasons, realizes that nothing at present moment can arrest the course of it.

E.T. Jaynes noted that Laplace's warning had gone unheeded by workers in the field.[4]

A reference class problem arises: the plausibility inferred will depend on whether we take the past experience of one person, of humanity, or of the earth. A consequence is that each referent would hold different plausibility of the statement. In Bayesianism, any probability is a conditional probability given what one knows. That varies from one person to another.

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Sunrise problem

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from Grokipedia
The Sunrise problem is a foundational thought experiment in probability and inductive reasoning, posed by the mathematician Pierre-Simon Laplace in his 1814 treatise A Philosophical Essay on Probabilities, which calculates the likelihood that the sun will rise tomorrow based on its observed risings over millennia of human history. Laplace applied his rule of succession—a Bayesian method assuming a uniform prior distribution over the unknown probability p of sunrise on any given day—to derive the formula (n + 1)/(n + 2), where n represents the number of past observed sunrises; for n = 1,826,213 (corresponding to roughly 5,000 years of daily risings), this yields a probability of 1,826,214 / 1,826,215, or approximately 0.99999945, framing the event as a bet of 1,826,214 to 1 in favor. This approach quantifies inductive confidence by treating the sunrise probability as drawn from a uniform distribution between 0 and 1, updated via observed successes, and assumes independence across days despite underlying physical determinism from Earth's rotation and orbit. Philosophically, the problem highlights the limitations of induction, building on David Hume's 1748 Enquiry Concerning Human Understanding, which argued that no logical necessity compels the future to resemble the past, rendering predictions like the sunrise unprovably certain despite empirical regularity. Laplace's solution offers a probabilistic mitigation, but critics note its assumptions—such as event independence and ignorance priors—fail for deterministic systems like , where modern astronomy confirms near-certainty via gravitational stability and no imminent disruptions, though absolute proof remains elusive. The problem continues to influence debates in , statistics, and , underscoring the tension between and foundational uncertainty in forecasting.

Problem Formulation

Core Statement

The sunrise problem is a classic probability puzzle that asks: given that the sun has risen every day for the past n consecutive days, what is the probability that it will rise on the (n+1)th day? In a well-known , Laplace considered n ≈ 1,826,213, corresponding to approximately 5,000 years of daily sunrises, to emphasize the problem's application to long sequences of observed events. This underscores the inductive challenge of inferring future outcomes from a finite history of observations, particularly when no underlying causal mechanism—such as gravitational laws or astronomical models—is presupposed or known. Without such , the problem forces reliance solely on empirical regularity, highlighting the inherent in extrapolating patterns to unseen instances. For the extreme case of n=0, where no prior sunrises have been observed, a naive assumption of uniformity over possible outcomes yields a probability of 1/2 for the , representing complete updated by no . The problem models daily sunrises as independent events, akin to repeated Bernoulli trials with an unknown success probability p, where each trial represents a day and success denotes a sunrise. Laplace's provides the classic probabilistic solution to this setup.

Historical Context

The sunrise problem originated with , who introduced it in his 1814 work Essai philosophique sur les probabilités as a means to illustrate the application of to uncertain future events based on historical observations. In this text, Laplace used the example of the sun's daily rising to demonstrate how repeated past occurrences could inform probabilistic predictions about tomorrow, calculating high odds in favor based on approximately 5,000 years of recorded risings. This formulation served as a practical within his broader exposition on the principles of probability, emphasizing its role in bridging empirical data and foresight. Laplace's presentation of the problem echoed the broader philosophical challenge of induction, particularly Hume's 18th-century regarding the justification for extrapolating past regularities to future expectations, as articulated in his 1748 Enquiry Concerning Human Understanding. Hume had questioned the rational basis for assuming uniformity in nature, using the sun's rising as a key to highlight the limits of without custom or habit. Laplace, in contrast, sought to address this through mathematical rigor, motivated by the era's advancements in and the need to quantify plausibility in everyday beliefs about natural phenomena like the sun's predictable cycle. The problem received attention in 19th-century probability literature, where it was referenced in historical surveys as an exemplar of , such as in Isaac Todhunter's 1865 A History of the Mathematical Theory of Probability from the Time of Pascal to That of Laplace. It gained renewed prominence in the 20th century amid discussions in and the revival of , notably in John Maynard Keynes's 1921 A Treatise on Probability, which critiqued and contextualized Laplace's approach within debates on inductive logic.

Probabilistic Approaches

Laplace's Rule of Succession

Laplace's rule of succession provides a method for estimating the probability of a future event based on a sequence of past observations, particularly when the underlying probability is unknown. In its basic form, if an event has occurred successfully s times in s + f trials, where f represents the number of failures (and f = 0 for the case of no failures), the probability that the event will succeed on the next trial is given by \frac{s + 1}{s + 2}. This formula arises from a of inductive , where Laplace sought to quantify the plausibility of continued success in repeatable trials without prior knowledge of the governing probability. The rationale for this rule rests on the assumption of insufficient reason: when no information is available about the true probability p of the event (where 0 ≤ p ≤ 1), all values of p are considered equally likely a priori. Laplace expressed this by integrating over the possible values of p, treating the probability of the next success as the expected value under this uniform distribution. In his original presentation, this yields the succession formula directly as \frac{s + 1}{s + 2} without invoking modern probabilistic frameworks. This approach corresponds to using a uniform prior distribution on p, which is mathematically equivalent to a Beta(1,1) distribution in contemporary terms. In application to the sunrise problem, Laplace illustrated the rule using the historical observation of the sun rising every day for approximately 5,000 years prior to his writing, amounting to about 1,826,213 daily sunrises without failure. Under the rule, the probability that the sun will rise the following day is then \frac{1{,}826{,}213 + 1}{1{,}826{,}213 + 2} = \frac{1{,}826{,}214}{1{,}826{,}215}, which evaluates to approximately 0.9999995. This high probability reflects the inductive strength drawn from the vast number of consistent observations, though it remains strictly less than 1 to account for epistemic uncertainty.

Bayesian Derivation

In the Bayesian framework, the sunrise problem is modeled by assuming that each daily sunrise is an independent and identically distributed (i.i.d.) Bernoulli trial with an unknown success probability pp, where the prior distribution for pp is uniform over [0,1], equivalent to a Beta(1,1) distribution. This prior reflects initial ignorance about pp, assigning equal plausibility to all values between 0 and 1. Given nn observed sunrises (all successes, no failures), the likelihood is pnp^n, and the posterior distribution updates to Beta(n+1n+1, 1). The predictive probability for the next sunrise is then the expected value of pp under this posterior, computed as the integral ∫01p⋅Γ(n+2)Γ(n+1)Γ(1)pn(1−p)0 dp=n+1n+2\int_0^1 p \cdot \frac{\Gamma(n+2)}{\Gamma(n+1)\Gamma(1)} p^n (1-p)^0 \, dp = \frac{n+1}{n+2}. This yields the probability n+1n+2\frac{n+1}{n+2} that the sun will rise on the (n+1)(n+1)-th day. More generally, with a Beta(α\alpha, β\beta) prior and observations of ss successes and ff failures, the posterior is Beta(α+s\alpha + s, β+f\beta + f), and the predictive probability for the next success is α+sα+β+s+f\frac{\alpha + s}{\alpha + \beta + s + f}. In the sunrise context, this specializes to α=1\alpha = 1, β=1\beta = 1, s=ns = n, f=0f = 0, recovering n+1n+2\frac{n+1}{n+2}. This Bayesian approach offers key advantages by explicitly incorporating prior knowledge through the choice of hyperparameters α\alpha and β\beta, allowing flexibility beyond uniform ignorance; for instance, a Beta(2,1) prior introduces slight optimism about sunrises, yielding a higher predictive probability of n+2n+3\frac{n+2}{n+3}. This contrasts with Laplace's , which emerges as the special case of the uniform Beta(1,1) prior.

Analysis and Criticisms

Philosophical Implications

The sunrise problem exemplifies David Hume's , highlighting the logical gap between observed regularities—such as the sun rising daily—and expectations for future occurrences, thereby challenging the uniformitarian assumption that the future will resemble the past without additional justification. This illustration underscores Hume's argument that inductive inferences cannot be deductively validated, as past experiences provide no necessary connection to unobserved events, rendering beliefs in continued patterns philosophically precarious. Karl Popper's falsificationism offers a critique of probabilistic resolutions to the sunrise problem, asserting that while a single instance of the sun failing to rise would decisively falsify the of perpetual sunrise, repeated confirmations cannot probabilistically confirm it, as prior probabilities for universal claims remain zero and fails to elevate them. Popper viewed such inductive as irresolvable through probability alone, advocating instead for science's advancement via bold conjectures subject to potential refutation rather than accumulative verification. The problem emphasizes the role of subjective priors in rational belief updating, where Bayesian frameworks, as applied by Laplace, attempt to formalize induction but reveal epistemology's dependence on initial assumptions that shape how evidence alters degrees of confidence in hypotheses like perpetual sunrise. This dependency influences by illustrating that rational acceptance of inductive predictions requires reconciling epistemic probabilities with the avoidance of dogmatic certainty, prompting debates on whether complete confidence can ever be justified from finite observations. In contemporary contexts, the sunrise problem informs and , where inductive biases enable pattern prediction without causal understanding, akin to agents updating policies based on sequential observations much like estimating tomorrow's sunrise from historical data. Such parallels highlight ongoing epistemological challenges in AI, as systems rely on prior assumptions to generalize from limited , mirroring the problem's core tension between and extrapolation.

Limitations and Alternatives

One major limitation of probabilistic solutions like Laplace's , which relies on a uniform prior, is that it disregards substantial from astronomy, such as the predictable mechanism of causing daily sunrises with near-certainty unless disrupted by rare cataclysmic events. This approach treats the probability parameter as equally likely across all values without factual grounding, leading to estimates that fail to incorporate established physical explanations for the phenomenon. Additionally, the uniform prior tends to overestimate the likelihood of rare or unprecedented events, particularly in scenarios with limited observations, as it assigns a 50% probability to the in the absence of prior data, an outcome that contradicts intuitive expectations informed by basic scientific understanding. Another critical issue is the assumption of independence among successive events, which does not hold for sunrises since they are highly correlated due to underlying physical laws governing planetary motion; a on one day would likely propagate, rendering the model unrealistic and producing misleading predictions. This oversight ignores the deterministic aspects of , where sunrises are not random Bernoulli trials but outcomes of stable orbital dynamics, further undermining the applicability of purely inductive probabilistic methods. In response to these limitations, the frequentist perspective avoids assigning a probability to unique, non-repeatable events like tomorrow's sunrise altogether, as probabilities require a well-defined repeatable experiment or long-run , which is absent here. Objective Bayesian approaches address the uniform prior's shortcomings by using informative yet non-subjective priors, such as the (Beta(0.5, 0.5)), which provides a more robust, invariance-based foundation for updating beliefs and yields higher confidence in repeated successes compared to . Similarly, Pólya's urn model offers an alternative interpretation through the lens of exchangeability, where drawing a "success" ball reinforces future draws in a way that aligns with Laplace's posterior but emphasizes de Finetti's representation theorem for infinite exchangeable sequences, providing a conceptually distinct framework for without assuming strict independence. To refine these models empirically, incorporating elevates the predicted probability to nearly 1, accounting only for minuscule risks from external factors like impacts, estimated at around 10−1210^{-12} per day based on analyses of giant trajectories and near-Earth object observations. This adjustment highlights how domain-specific data can override generic priors, yielding predictions far more aligned with physical reality than uninformative inductive rules alone.
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