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We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called 'supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer-or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first-rather than second-class ticket, or rescheduling a trip (e.g. away from rush hours) when being offered a supersaver ticket. Predictive machine learning suggests that customer's age, demand-related information for a specific connection (like departure time and utilization), and the discount level permit forecasting buying behavior to a certain extent. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among 'always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics.

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IntroductionGambling can have serious consequences for many aspects of a person’s life. Yet relatively few people with gambling problems seek help. This study examines the extent to which exclusion from casino venues among other factors may act as a motivator for further help-seeking among casino gamblers (both landbased and remote) with at-risk or disordered gambling behavior. In addition, the barriers that prevent gamblers from accepting help are examined.MethodsGamblers from Swiss casinos completed a written questionnaire twice, at 6-month intervals. The questions included whether they had sought help in the past 6 months.ResultsFor those with a SOGS-R rating of 1 or over (n = 173) at the second survey point, a difference in help-seeking was found between the excluded and non-excluded gamblers (p < .001), suggesting that exclusion may be a motivator for help-seeking. Reported differences in levels of debt (p = .006), recognition of gambling problems (p = .010) and severity of gambling-related problems (p = .004) can be taken to suggest that other motivating factors may also influence help-seeking behavior. With regard to the support sought, the most frequently used forms of support were specialized addiction counseling centers (39.5%), followed by self-help groups (21.1%) and remote counseling centers (10.5%). In terms of barriers, reasons relating to attitude, such as denial, appear to pose greater barriers than treatment-related concerns.DiscussionFrom a public health perspective, an overarching strategy is required to increase the share of help-seekers among casino gamblers through targeted measures.