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In recent years, transformer-based language representation models (LRMs) haveachieved state-of-the-art results on difficult natural language understandingproblems, such as question answering and text summarization. As these modelsare integrated into real-world applications, evaluating their ability to makerational decisions is an important research agenda, with practicalramifications. This article investigates LRMs' rational decision-making abilitythrough a carefully designed set of decision-making benchmarks and experiments.Inspired by classic work in cognitive science, we model the decision-makingproblem as a bet. We then investigate an LRM's ability to choose outcomes thathave optimal, or at minimum, positive expected gain. Through a robust body ofexperiments on four established LRMs, we show that a model is only able to`think in bets' if it is first fine-tuned on bet questions with an identicalstructure. Modifying the bet question's structure, while still retaining itsfundamental characteristics, decreases an LRM's performance by more than 25\%,on average, although absolute performance remains well above random. LRMs arealso found to be more rational when selecting outcomes with non-negativeexpected gain, rather than optimal or strictly positive expected gain. Ourresults suggest that LRMs could potentially be applied to tasks that rely oncognitive decision-making skills, but that more research is necessary beforethey can robustly make rational decisions.

Can Language Representation Models Think in Bets?