Lærerstudenters bruk av Monte Carlo-simulering for å løse oppgaver om sannsynlighet: En analyse av 16 gruppebesvarelser

Programmering er tatt inn i den nye læreplanen for matematikk i grunnskolen i Norge. Det betyr at lærerstudenter har behov for å få erfaring med å løse matematiske problemer gjennom programmering. I matematikkfaget på lærerutdanninga for 1.–7. trinn ble det laga et undervisningsopplegg som omfatta opplæring i sannsynlighet og Monte Carlo-simulering med programmering i Excel med Visual Basic for Applications (VBA). Arbeidskravet innebar bruk av Monte Carlo-simulering for å løse Chevalier de Méré-problemet og Monty Hall-problemet. I etterkant av studentenes arbeid ble det utforma en NSD-godkjent studie. Utvalget i denne studien er 16 studentgruppers besvarelser på arbeidskravet knytta til dette undervisningsopplegget innen dataprogrammering i mate­matikkfaget. Et funn fra studien er at småfeil kan skape store problemer ettersom mange studenter ikke klarer å vurdere hvor fornuftige de svarene programmet gir er. I tillegg gir manglende systematikk feilsvar. Men i de tilfellene der studentene klarer å program­mere rett, hjelpes de til å løse Chevalier de Méré-problemet. Vi finner også at studentene kan få hjelp av manuell Monte Carlo-simulering for å løse Monty Hall-problemet, gitt at denne gir tallverdier som ligger nært forventningsverdien (p = 2/3), mens i de tilfel­lene hvor tallverdiene ligger langt unna forventningsverdien kan det virke forvirrende. Det er fordeler og ulemper med både manuell og digital Monte Carlo-simulering, og det ser ut til at lærerstudenter kan ha nytte av å løse oppgaver ved hjelp av begge metoder. For å få det beste læringsutbyttet er det avgjørende at læreren velger gode og relevante oppgaver, som gjør at studentene både ser nytten av simuleringa, og også har en viss mulighet til å kontrollere svaret, slik at ikke tilfeldighet under simuleringa og program­meringsfeil bidrar til forvirring.

One game show, two boys, two aces, three prisoners - what’s an AI to do?

We review a quartet of widely discussed probability puzzles – Monty Hall, the three prisoners, the two boys, and the two aces. Pearl explains why the Monty Hall problem is counterintuitive using a causal diagram. Glenn Shafer uses the puzzle of the two aces to justify reintroducing to probability theory protocols that specify how the information we condition on is obtained. Pearl, in one treatment of the three prisoners, adds to his representation random variables that distinguish actual events and observations. The puzzle of the two boys took a perplexing twist in 2010. We show the puzzles have similar features, and each can be made to give different answers to simple queries corresponding to different presentations of the word problem. We offer a unified treatment that explains this phenomenon in strictly technical terms, as opposed to cognitive or epistemic.

Monty Hall three door ’anomaly’ revisited: a note on deferment in an extensive form game

The Monty Hall game is one of the most discussed decision problems, but where a convincing behavioral explanation of the systematic deviations from probability theory is still lacking. Most people not changing their initial choice, when this is beneficial under information updating, demands further explanation. Not only trust and the incentive of interestingly prolonging the game for the audience can explain this kind of behavior, but the strategic setting can be modeled more sophisticatedly. When aiming to increase the odds of winning, while Monty’s incentives are unknown, then not to switch doors can be considered as the most secure strategy and avoids a sure loss when Monty’s guiding aim is not to give away the prize. Understanding and modeling the Monty Hall game can be regarded as an ideal teaching example for fundamental statistic understandings.

Sleeping Beauty on Monty Hall

Inspired by the Monty Hall Problem and a popular simple solution to it, we present a number of game-show puzzles that are analogous to the notorious Sleeping Beauty Problem (and variations on it), but much easier to solve. We replace the awakenings of Sleeping Beauty by contestants on a game show, like Monty Hall’s, and increase the number of awakenings/contestants in the same way that the number of doors in the Monty Hall Problem is increased to make it easier to see what the solution to the problem is. We show that these game-show proxies for the Sleeping Beauty Problem and variations on it can be solved through simple applications of Bayes’s theorem. This means that we will phrase our analysis in terms of credences or degrees of belief. We will also rephrase our analysis, however, in terms of relative frequencies. Overall, our paper is intended to showcase, in a simple yet non-trivial example, the efficacy of a tried-and-true strategy for addressing problems in philosophy of science, i.e., develop a simple model for the problem and vary its parameters. Given that the Sleeping Beauty Problem, much more so than the Monty Hall Problem, challenges the intuitions about probabilities of many when they first encounter it, the application of this strategy to this conundrum, we believe, is pedagogically useful.