20 th ICABR Conference on: TRANSFORMING THE BIOECONOMY: BEHAVIOR, INNOVATION AND SCIENCE June 26-29, 2016 Ravello, Italy Stuart Smyth University of Saskatchewan Canada UNIVERSITY OF SASKATCHEWAN

Structure of Talk 2 Background Literature review ModelMethodResultsSummaryImplications

3 What? (Facts) Global investment in agri-food research about $40 billion (2009) Less than 1% of bioscience innovations are successful (Graff et al 2009) The cost and time to get to market is high, rising and less certain Why? (Problem) Delays in decision making within institutions and among actors in the global policy regime How? (Solution) Effective, efficient and timely decisions are critical to ensuring that new agri-food technologies reach the marketplace to benefit industry and global consumers Background/Literature/Model/Method/Results/Summary/Implications What is the story?

What are the objective & contributions? Objective → To investigate risk preferences of both the general public and experts regarding innovation and new technology applications in the agri-food industry in Canada and the USA Contributions → Basic risk preferences + PROVENANCE (e.g. whether technology is being provided by a public university or a multinational corporation) → Context of the study: AGRICULTURE (Replication of Tversky and Kahneman’s Asian disease problem in the context of agricultural technologies) 4 Background/Literature/Model/Method/Results/Summary/Implications

Literature review Are experts and non-experts’ decisions different? → Mixed research findings: – Non-existence of framing effects for experts (Fagley et al. 1999, Druckman 2004) – Similar framing effects for experts and novices (Loke and Tan 1992) – Experts overcome framing effects better than laypersons (Korobkin and Guthrie 1998, Guthrie and Rachlinski 2006, Okoli et al. 2013, Belton et al. 2014). Experts are affected by framing; however, they are more rational and exhibit less risk-aversion behaviour under the positive framing (e.g. Fatas et al. 2004, Potters & van Winden 2000, Dyer et al. 1989) Are risk preferences affected by information framing? → Mixed results: – Few studies found NO significant framing effects (Miller and Fagley 1991) – Majority of studies observed consistent framing effects, the magnitude of the effect tends to be lower than the original experiment (Druckman 2001, Levin et al. 1998). 5 Background/Literature/Model/Method/Results/Summary/Implications

Structure of the Asian disease problem FrameChoice optionConsequenceExpected valueStudy results (% of respondents) PositiveProspect A200 saved200 saved (400 dead)72% Prospect B(1/3) 600 saved200 saved (400 dead)28% NegativeProspect C400 die400 dead (200 saved)22% Prospect D(2/3) 600 die400 dead (200 saved)78% 6 Model: The Asian Disease (Tverksy & Kahneman 1981) → E(prospect A) = E(prospect B) → A and C are logically equivalent (riskless choices) → E(prospect C) = E(prospect D)→ B and D are logically equivalent (risky choices) H1: The rate of choice will be 50% for each of prospects A and B. H2: H1 will hold for the positively-framed, and the negatively-framed sets of prospects A and B. → Tverksy & Kahneman 1981: People tend to be risk averse when exposed to gains and risk seeking when exposed to losses Background/Literature/Model/Method/Results/Summary/Implications

Method: Online survey, 2 experiments, 4 groups A, B, C & D 7 Experiment I: Positive and negative framings (crops saved/crops lost) Experiments II: Provenance treatment (Public University/Multinational Corporation) Severe drought conditions (P F ) Spread of an unusual Fungus (P F P ) Severe environmental conditions (P F E ) + / - framings No provenanceProvenance No Provenance 2014 Data A: N=574: US general public (Mturk) B: N=310: International stakeholders involved in regulating new crop varieties (LinkedIn/ ) 2015 Data C: N=500: Canadian public (Probit) D: N= 109: International experts (Onsite) H3: A choice shift between the positive and negative conditions of P F, P F P and P F E will be observed for both general public (A and C) and experts (B and D) H4: The magnitude of the choice shift is significantly different for the expert groups (B and D) and for the general public groups (A and C). Background/Literature/Model/Method/Results/Summary/Implications

8 A: US laypersons - H1 & H2 rejected (p<.00001): US laypersons were affected by framing in their choice of prospects - H3 could not be rejected for positive and negative frames → A choice shift was observed: 86.1% of subjects chose the riskless option when presented with the positively- framed problem and 72.7% chose the riskless option when presented with the negatively-framed problem. → Weak choice shift = 13.4%. B: International experts - H1 & H2 fail to be rejected (p=0.6312) under the negatively- and positively-framed problem → Experts experience a higher impact from the positive-format framing than the negative format, 82% chose the riskless option in the +ve framing while 52% chose the riskless option in –ve framing - H3 fails to be rejected → a moderate choice shift of 28.2% observed in Group B for both positive and negative frames Comparing A & B - Significant difference between the choices in the loss format → The general public (A) was significantly more risk-averse than the experts (B) in the loss format - H4: not rejected: the magnitude of the choice shift is significantly higher for B than for A (28.2% vs 13.4%) → When the problem is expressed in terms of producers’ crops lost, experts become significantly less risk-averse than in the gain format. - For both groups, the probability of choosing the riskless option (Tech A) over the risky option (Tech B) was much higher in the positively-framed question than in the negative question. Result 1: Framing manipulation in A and B (Severe drought conditions) Background/Literature/Model/Method/Results/Summary/Implications

9 Result 2: Framing & provenance manipulations in A and B ( Spread of an unusual Fungus ) A: US laypersons - H1 & H2 rejected (p<.00001) for public university and multinational corporations - H3 failed to be rejected for positive and negative frames → A weak choice shift for both provenance treatments (PU & MC) B: International experts - H1 & H2 fail to be rejected (p=0.37, p=.36 ) under the negative and positive frames of PU treatment. - H3 is rejected for PU → No significant evidence for a choice shift or choice reversal under +/- formats for PU. - H1 & H3 is rejected in the gain format for MC - H2 failed to be rejected for MC under +/- format → evidence of a choice shift Comparing A & B - When provenance is PU → significant difference (p= ) → In the positively-framed scenario experts (Group B) are less risk-averse than laypersons (Group A) - For both frames of the MC → no statistically significant differences between the general public and the experts’ choices. (H2 was rejected as the magnitude of the choice shift was not significantly different between B and A). → Neither PU nor MC had a significant statistical effect in the probability of choosing prospect A over prospect B. Background/Literature/Model/Method/Results/Summary/Implications

10 Result 3: Framing manipulation in C and D (Severe environmental conditions) C: Canada laypersons - H1 & H2 rejected (p<.00001) for framing manipulations - H3 failed to be rejected → A weak choice shift of 11.1% : 81.5% of subjects chose the riskless option when presented with the positively-framed problem and 70.4% chose the riskless option when presented with the negatively- framed problem. D: International experts - H1 rejected (p=0.0002) in the gain format - H1 fails to be rejected (p=0.45) in the loss format - H2 rejected → Experts respond differently to framing: 63.6% opted for the riskless choice in the positive framing compared to 43.8% who opted for the risky choice in the negative frame. - H3 fails to be → Evidence for a choice shift → Risk-aversion is more strongly induced by the positive condition than risk seeking is induced by the negative condition Comparing C & D - Rates of riskless choice in C (81.5% and 70.4%) and D (63.6% and 43.8%) were significantly different (p= and respectively) → Canadian general public (Group C) is significantly more risk-averse than experts (Group D). → Framing had a significant impact on participants’ choices: the probability of choosing the riskless option (Tech A) over the risky one (Tech B) was much higher in the gain format than in the loss format (Wald Chi-Square of and respectively). Background/Literature/Model/Method/Results/Summary/Implications

11 Result 4: Framing & provenance in C and D (Severe environmental conditions) C: Canada laypersons (Same results as for A) - H1 & H2 rejected; H3 failed to be rejected for both framing and provenance treatments → the Canadian general public, like in the US, exhibits strong risk-aversion → A weak choice shift of 19% & 11.5%: for both provenance manipulations → Combined with framing, provenance manipulation did not change participants’ choices and attitudes towards risk. D: International experts - H1 rejected in the positive frame, H1 fails to be rejected the negative frame for both provenance treatments → H2 rejected → Positive framing triggers more riskless choice than negative framing triggers riskier choices - H3, H4 rejected → The magnitude of the choice shift is not significantly different for D from C Comparing C & D - In the loss format with the PU treatment, both experts and laypersons tolerate more risk, while for the MC experts (Group D) are willing to make riskier choices than the general public → Experts (regulators and industry experts) will have more experience with and confidence in private firms than the general public → For laypersons (C) framing drives choice, not provenance → For experts (D), there is no statistically significant combined effect of framing and provenance on participants’ choices Background/Literature/Model/Method/Results/Summary/Implications

12 Key take-home messages A: US public risk-aversion significant framing effects weak provenance effects B: Experts less risk-averse than the US lay public robustly responsive to framing effects weakly influenced by provenance C: Canadian public risk-averse inconsistent effects for framing and provenance D: Experts significantly less risk-averse than C strong evidence of framing no-provenance impact → Framing manipulations triggered choice shifts, where positive framing induced risk-aversion more than negative framing induced risk seeking, and some provenance effects → The general population (A & C) is significantly more risk-averse than the experts (B & D). → Both groups of experts (B & D), had the same attitude towards risk (less risk-averse) for all manipulations → Both public groups (A & C) exhibit strong risk-aversion. Background/Literature/Model/Method/Results/Summary/Implications

Policy recommendations  Framing has a strong impact on all participants’ choices, yet there is a weak and inconsistent provenance effect across results → Regulatory and policy systems can and should recognize the importance of framing on decision making and work to normalize the frames to minimize the effect of framing and provenance on risk decisions.  The differences between expert and lay risk preferences goes a long way to explaining why regulatory decisions might not be universally accepted and respected, even if they are appropriately delivered by experts  Even in a science-based regulatory system, such as Canada’s regime for PNTs, expert risk decisions can be affected by non-scientific issues such as framing. → Good regulatory practice would suggest that regulatory packages should by practice be put into a single frame to normalize for this effect.  Provenance matters both for experts and laypersons, creating the potential for biased and inconsistent decision making in the public space. → Set up a special review where the provenance of the evidence is anonymized to reduce the potential cognitive impact.  Decision architecture does matter. → Evidence that the regulatory system for PNTs in Canada delivers irregular decisions that might be driven by the cognitive limitations (Clark and Phillips 2013) → More research on the impact of architecture is warranted 13 Background/Literature/Model/Method/Results/Summary/Implications

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