Hypothetical bias continues to be a challenge for practitioners of stated preference methods, and various remedies have been proposed to mitigate the problem. This paper presents the background, theory, and experimental design for testing two novel hypothetical bias mitigation treatments in the context of a contingent-valuation survey focused on a beach conditions monitoring service for Gulf Coast beachgoers. The two treatments proposed are: 1) a multiple-question budget and substitutes treatment, and 2) a cheap talk with confirmation treatment, to be tested both independently and in tandem. We present a theoretically-consistent model of budget-constrained utility maximization which accounts for the respondents’ subjective probability of a good beach trip with and without the beach conditions information. Data are to be collected via an online referendum-style valuation questionnaire sent to a randomly-selected sample of Gulf Coast households. Along with referendum responses and subjective probabilities, other information elicited from the respondents will include beach visit frequency, beach activities engaged in, knowledge of existing monitoring services, and specific beach conditions of interest.