Value-based Decision Research Articles

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions.

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision‐making and other‐regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms—pertinent to a broad range of value‐based decisions—and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision‐making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research.This article is categorized under:Economics > Interactive Decision‐MakingPsychology > Emotion and MotivationNeuroscience > CognitionEconomics > Individual Decision‐Making

OCD Influences Evidence Accumulation During Decision Making in Males but Not Females During Perceptual and Value-Driven Choice.

Individuals with obsessive-compulsive disorder (OCD) often have difficulty making decisions. Valuation and value-based judgements are particularly difficult. The mechanisms underlying these impairments are still poorly understood. Previous work has suggested that individuals with OCD require more information prior to making a choice during perceptual discrimination tasks. Little previous work has examined value-guided choice in OCD. Here we examined perceptual and value-based decision making in adults with OCD, using a novel task in which the two types of decision are tested in parallel using the same individually calibrated sets of visual stimuli (Perceptual and Value-based decision-making task, PVDM). Twenty-seven unmedicated participants with OCD (16 female) and thirty-one healthy controls (15 female) were tested. Data were analyzed using hierarchical drift-diffusion modeling (HDDM). Decision formation was altered in OCD, but differentially between genders: males with OCD, but not females, accumulated more information (i.e., were more cautious) and were less effective in evidence accumulation than age- and IQ-matched healthy males. Furthermore, males with OCD, but not females, were less likely than controls to adjust the process of evidence accumulation across decision contexts. These unexpectedly gender-dimorphic effects suggest that more attention should be paid to gender differences in studies of OCD, and of pathophysiology more broadly.

Value-based cognition and drug dependency

Value-based decision-making is thought to play an important role in drug dependency. Achieving elevated levels of euphoria or ameliorating dysphoria/pain may motivate goal-directed drug consumption in both drug-naïve and long-time users. In other words, drugs become viewed as the preferred means of attaining a desired internal state. The bias towards choosing drugs may affect one’s cognition. Observed biases in learning, attention and memory systems within the brain gradually focus one’s cognitive functions towards drugs and related cues to the exclusion of other stimuli. In this narrative review, the effects of drug use on learning, attention and memory are discussed with a particular focus on changes across brain-wide functional networks and the subsequent impact on behaviour. These cognitive changes are then incorporated into the cycle of addiction, an established model outlining the transition from casual drug use to chronic dependency. If drug use results in the elevated salience of drugs and their cues, the studies highlighted in this review strongly suggest that this salience biases cognitive systems towards the motivated pursuit of addictive drugs. This bias is observed throughout the cycle of addiction, possibly contributing to the persistent hold that addictive drugs have over the dependent. Taken together, the excessive valuation of drugs as the preferred means of achieving a desired internal state affects more than just decision-making, but also learning, attentional and mnemonic systems. This eventually narrows the focus of one’s thoughts towards the pursuit and consumption of addictive drugs.

Social preferences correlate with cortical thickness of the orbito-frontal cortex.

Humans differ in their preferences for personal rewards, fairness and others’ welfare. Such social preferences predict trust, public goods provision and mutual gains bargaining and have been linked to neural activity in regions involved in reward computation, cognitive control and perspective-taking. Although shaped by culture, social preferences are relatively stable across time, raising the question whether differences in brain anatomy predict social preferences and their key components—concern for personal outcomes and concern for others’ outcomes. Here, we examine this possibility by linking social preferences measured with incentivized economic games to 74 cortical parcels in 194 healthy humans. Neither concerns for personal outcomes nor concerns for the outcomes of others in isolation were related to anatomical differences. However, fitting earlier findings, social preferences positively scaled with cortical thickness in the left olfactory sulcus, a structure in the orbital frontal cortex previously shown to be involved in value-based decision-making. Consistent with work showing that heavier usage corresponds to larger brain volume, findings suggest that pro-social preferences relate to cortical thickness in the left olfactory sulcus because of heavier reliance on the orbital frontal cortex during social decision-making.

Problems with Social Cognition and Decision-Making in Huntington's Disease: Why Is it Important?

Huntington’s disease starts slowly and progresses over a 15–20 year period. Motor changes begin subtly, often going unnoticed by patients although they are typically visible to those close to them. At this point, it is the early non-motor problems of HD that arguably cause the most functional impairment. Approximately 65% of gene carriers will experience a reduction in their occupational level, and just under half will feel unable to manage their finances independently before a clinical diagnosis is made. Understanding what drives this impairment in activities of daily living is the key to helping people with HD to live more independently for longer, especially in early disease. Early cognitive decline is likely to play a contributory factor although few studies have looked directly at this relationship. Recently, it has been shown that along with the well documented dysexecutive syndrome seen in HD, changes in social cognition and decision-making are more common than previously thought. Furthermore, some of the early neuropathological and neurochemical changes seen in HD disrupt networks known to be involved in social functioning. In this review, we explore how HD changes the way individuals interact in a social world. Specifically, we summarise the literature on both classical and social decision-making (value-based decision-making in a social context) along with studies of theory of mind, empathy, alexithymia, and emotion recognition in HD. The literature specific to HD is discussed and supported by evidence from similar neurodegenerative disorders and healthy individuals to propose future directions and potential therapeutic avenues to be explored.

The role of anticipated regret in choosing for others

In everyday life, people sometimes find themselves making decisions on behalf of others, taking risks on another’s behalf, accepting the responsibility for these choices and possibly suffering regret for what they could have done differently. Previous research has extensively studied how people deal with risk when making decisions for others or when being observed by others. Here, we asked whether making decisions for present others is affected by regret avoidance. We studied value-based decision making under uncertainty, manipulating both whether decisions benefited the participant or a partner (beneficiary effect) and whether the partner watched the participant’s choices (audience effect) and their factual and counterfactual outcomes. Computational behavioural analysis revealed that participants were less mindful of regret (and more strongly driven by bigger risks) when choosing for others vs for themselves. Conversely, they chose more conservatively (regarding both regret and risk) when being watched vs alone. The effects of beneficiary and audience on anticipated regret counteracted each other, suggesting that participants’ financial and reputational interests impacted the feeling of regret independently.

The Impact of the Storm Prediction Center's Convective Outlooks and Watches on Emergency Management Operational Planning

For many emergency managers (EMs) and National Weather Service (NWS) forecasters, Convective Outlooks issued by the Storm Prediction Center (SPC) influence the preparation for near-term severe weather events. However, research into how and when EMs utilize that information, and how it influences their emergency operations plan, is limited. Therefore, to better understand how SPC Convective Outlooks are used for severe weather planning, a survey was conducted of NWS core partners in the emergency management sector. The results show EMs prefer to wait until an Enhanced Risk for severe thunderstorms is issued to prepare for severe weather. In addition, the Day 2 Convective Outlook serves as the threshold for higher, value-based decision making. The survey was also used to analyze how the issuance of different risk levels in SPC Convective Outlooks impact emergency management preparedness compared to preparations conducted when a Convective Watch is issued.

Ventromedial Prefrontal-Anterior Cingulate Hyperconnectivity and Resilience to Apathy in Traumatic Brain Injury.

Apathy is a common and impairing sequela of traumatic brain injury (TBI). Yet, little is known about the neural mechanisms determining in which patients apathy does or does not develop post-TBI. We aimed to elucidate the impact of TBI on motivational neural circuits and how this shapes apathy over the course of TBI recovery. Resting-state functional magnetic resonance imaging data were collected in patients with subacute mild TBI (n = 44), chronic mild-to-moderate TBI (n = 26), and nonbrain-injured control participants (CTRL; n = 28). We measured ventromedial prefrontal cortex (vmPFC) functional connectivity (FC) as a function of apathy, using an a priori vmPFC seed adopted from a motivated decision-making study in an independent TBI study cohort. Patients reported apathy using a well-validated tool for assaying apathy in TBI. The vmPFC-to-wholebrain FC was contrasted between groups, and we fit regression models with apathy predicting vmPFC FC. Subacute and chronic TBI caused increased apathy relative to CTRL, replicating previous work suggesting that apathy has an enduring impact in TBI. The vmPFC was functionally connected to the canonical default network, and this architecture did not differ between subacute TBI, chronic TBI, and CTRL groups. Critically, in TBI, increased apathy scores predicted decreased vmPFC-dorsal anterior cingulate cortex (dACC) FC. Last, we subdivided the TBI group based on patients above versus below the threshold for "clinically significant apathy," finding that TBI patients with clinically significant apathy demonstrated comparable vmPFC-dACC FC to CTRLs, whereas TBI patients with subthreshold apathy scores demonstrated vmPFC-dACC hyperconnectivity relative to both CTRLs and patients with clinically significant apathy. Post-TBI vmPFC-dACC hyperconnectivity may represent an adaptive compensatory response, helping to maintain motivation and enabling resilience to the development of apathy after neurotrauma. Given the role of vmPFC-dACC circuits in value-based decision making, rehabilitation strategies designed to improve this ability may help to reduce apathy and improve functional outcomes in TBI.

Defensive freezing and its relation to approach\u2013avoidance decision-making under threat

Successful responding to acutely threatening situations requires adequate approach–avoidance decisions. However, it is unclear how threat-induced states—like freezing-related bradycardia—impact the weighing of the potential outcomes of such value-based decisions. Insight into the underlying computations is essential, not only to improve our models of decision-making but also to improve interventions for maladaptive decisions, for instance in anxiety patients and first-responders who frequently have to make decisions under acute threat. Forty-two participants made passive and active approach–avoidance decisions under threat-of-shock when confronted with mixed outcome-prospects (i.e., varying money and shock amounts). Choice behavior was best predicted by a model including individual action-tendencies and bradycardia, beyond the subjective value of the outcome. Moreover, threat-related bradycardia (high-vs-low threat) interacted with subjective value, depending on the action-context (passive-vs-active). Specifically, in action-contexts incongruent with participants’ intrinsic action-tendencies, stronger bradycardia related to diminished effects of subjective value on choice across participants. These findings illustrate the relevance of testing approach–avoidance decisions in relatively ecologically valid conditions of acute and primarily reinforced threat. These mechanistic insights into approach–avoidance conflict-resolution may inspire biofeedback-related techniques to optimize decision-making under threat. Critically, the findings demonstrate the relevance of incorporating internal psychophysiological states and external action-contexts into models of approach–avoidance decision-making.

Transforming absolute value to categorical choice in primate superior colliculus during value-based decision making

Value-based decision making involves choosing from multiple options with different values. Despite extensive studies on value representation in various brain regions, the neural mechanism for how multiple value options are converted to motor actions remains unclear. To study this, we developed a multi-value foraging task with varying menu of items in non-human primates using eye movements that dissociates value and choice, and conducted electrophysiological recording in the midbrain superior colliculus (SC). SC neurons encoded “absolute” value, independent of available options, during late fixation. In addition, SC neurons also represent value threshold, modulated by available options, different from conventional motor threshold. Electrical stimulation of SC neurons biased choices in a manner predicted by the difference between the value representation and the value threshold. These results reveal a neural mechanism directly transforming absolute values to categorical choices within SC, supporting highly efficient value-based decision making critical for real-world economic behaviors.

Body dynamics of gait affect value-based decisions

Choosing among different options typically entails weighing their anticipated costs and benefits. Previous research has predominantly focused on situations, where the costs and benefits of choices are known before an action is effectuated. Yet many decisions in daily life are made on the fly, for instance, making a snack choice while walking through the grocery store. Notably, the costs of actions change dynamically while moving. Therefore, in this study we examined whether the concurrent action dynamics of gait form part of and affect value-based decisions. In three experiments, participants had to decide which lateral (left vs. right) target (associated with different rewards) they would go to, while they were already walking. Results showed that the target choice was biased by the alternating stepping behavior, even at the expense of receiving less reward. These findings provide evidence that whole-body action dynamics affect value-based decisions.

More than just statics: Temporal dynamic changes of intrinsic brain activity in cigarette smoking.

Smoking is companied with altered intrinsic activity of the brain measured by amplitude of low-frequency fluctuation. Evidence has revealed that human brain activity is a highly dynamic and rapidly changing system. How exactly cigarette smoking affect temporal dynamic intrinsic brain activity is not fully understood nor is it clear how smoking severity influences spontaneous brain activity. Dynamic amplitude of low-frequency fluctuation (dALFF) was used to examine the dynamic temporal variability in 93 participants (63 smokers, 30 nonsmokers). We further divided smokers into light and heavy smokers. The temporal variability in intrinsic brain activity among these groups was compared. Correlation analyses were performed between dALFF in areas showing group differences and smoking behaviour (e.g., the Fagerström Test for Nicotine Dependence [FTND] scores and pack-years). Smokers showed significantly increased dALFF in the left inferior/middle frontal gyrus, right orbitofrontal gyrus, right insula, left superior/medial frontal gyrus and right middle frontal gyrus than nonsmokers. Light smokers showed increased dALFF variability in the left prefrontal cortex. Heavy smokers showed increased dynamics in specific brain regions, including the right postcentral gyrus, right insula and left precentral gyrus. Furthermore, the temporal variability in dALFF in the left superior/medial frontal gyrus, left superior/middle frontal gyrus, right middle frontal gyrus and right insula was positively correlated with pack-years or FTND. Combined, these results suggest that smokers increase stable and persistent spontaneous brain activity in prefrontal cortex, involved impaired gold-directed action and value-based decision-making. In addition, individuals with heavier smoking severity show increased perturbance on spontaneous brain activity of perception and sensorimotor, related to increased reliance.

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

Subjective value, not a gridlike code, describes neural activity in ventromedial prefrontal cortex during value-based decision-making

Across many studies, ventromedial prefrontal cortex (vmPFC) activity has been found to correlate with subjective value during value-based decision-making. Recently, however, vmPFC has also been shown to reflect a hexagonal gridlike code during navigation through physical and conceptual space, and such gridlike codes have been proposed to enable value-based choices between novel options. Here, we first show that, in theory, a hexagonal gridlike code can in some cases mimic vmPFC activity previously attributed to subjective value, raising the possibility that the subjective value correlates previously observed in vmPFC may have actually been a misconstrued gridlike signal. We then compare the two accounts empirically, using fMRI data from a large number of subjects performing an intertemporal choice task. We find clear and unambiguous evidence that subjective value is a better description of vmPFC activity in this task than a hexagonal gridlike code. In fact, we find no significant evidence at all for a hexagonal gridlike code in vmPFC activity during intertemporal choice. This result limits the generality of gridlike modulation as description of vmPFC activity. We suggest that vmPFC may flexibly switch representational schemes so as to encode the most relevant information for the current task.

Accumbal D1 and D2 medium spiny neurons control distinct learning parameters in complex behavior

Value-based decision-making is at the core of nearly all motivated behaviors and requires the ability to associate outcomes with specific actions and make adaptive decisions about future behavioral action. At the core of value-based decision-making and reinforcement is the nucleus accumbens (NAc), which is integrally involved in learning, selecting, and executing goal-oriented behaviors. The NAc is a heterogeneous population primarily composed of D1 and D2 medium spiny projection (MSN) neurons that are thought to have opposing roles in behavior, in which D1 MSNs promote reward and D2 MSNs promote aversion. By expressing channelrhodopsin selectively in D1- and D2- populations (using D1-Cre and A2A-Cre mice) in the NAc core, we show that mice will nose poke for optical self-stimulation of both cell types, suggesting D2-MSN activity is not inherently aversive. While optogenetic approaches give some information about how cellular activation can modulate behavior, they eliminate the temporally specific neural activity patterns that encode information in behaving animals. To understand how real-time activity in these populations is linked to behavioral execution, we expressed the genetically encoded calcium indicator (GCaMP6f) within D1 and D2 MSNs coupled with in vivo fiber photometry to record from these cell populations in awake and behaving animals during pavlovian and operant conditioning tasks. Utilizing complex reinforcement schedules that allow dissociation of stimulus value, outcome, cue learning, and action, we show that D1 MSNs respond to the presence and intensity of unconditioned stimuli – regardless of value. Conversely, D2 MSNs respond to a mismatch between what is expected and what is received and thus encode errors in prediction in a value-independent fashion. To establish a causal link between the information encoded within these neuronal populations and behavioral output, we next expressed an inhibitory opsin (halorhodopsin) selectively in either D1- or D2-MSNs to inhibit these cells at specific time points during a fear conditioning task. We show that inhibition at select time points, either by inhibiting D2-MSN activity during the predictive cue or D1-MSN activity during the shock outcome, disrupts learning of the association. We thus provide foundational evidence for the discrete aspects of information that are encoded within these cellular populations.

Input‐specific regulation of discrete populations of Lateral Habenula neurons by Kappa opioid receptors

The lateral habenula (LHb) is an epithalamic brain region associated with value-based decision making and stress evasion through its modulation of reward circuitry. Increased activity of the LHb is associated with drug addiction and stress-related mood disorders. Dynorphin (DYN)/Kappa opioid receptor (KOR) signaling is an endogenous mediator of stress response in reward circuitry. Previously, we have shown a novel functional role of DYN/KOR signaling within LHb and its dysregulation by a severe early life stress (maternal deprivation, MD). Specifically, KOR stimulation has distinct effects on LHb neuronal excitability in distinct neuronal populations identified by the presence of hyperpolarizing current (Ih) that were absent following MD. Namely, KOR stimulation through the KOR agonist, U50,488, significantly decreases excitability of Ih negative (Ih-) LHb neurons while simultaneously increasing excitability of Ih positive (Ih+) LHb neurons. Additionally, KOR-induced alteration in LHb excitability is dependent on both presynaptic glutamatergic and GABAergic signaling likely through presynaptic KORs on inputs to LHb neurons. Given that LHb inputs from the entopeduncular nucleus (EP) and lateral hypothalamus (LH) have been implicated in stress-induced mood disorders through modulation of LHb excitability, we hypothesize that the presynaptic effects of KOR stimulation on LHb neuronal activity may be driven by one or both of these inputs. Using selective in vitro optical stimulation of either EP or LH inputs to LHb neurons in brain slices from rats, our pilot study suggests that KOR activation of EP and LH inputs may differentially affect optically evoked action potential generation across Ih- and Ih+ LHb subpopulations. Moreover, our data from MD rats suggest that there may be an imbalance of excitation to inhibition at EP synapses onto LHb neurons. Given that MD increases immunolabeled DYN (the endogenous KOR agonist) labeling in neuronal fibers in LHb while significantly decreasing mRNA levels of KORs in LHb, we also investigated MD-induced modulation of a KOR-specific downstream signaling (p38 MAPK). Our molecular data indicate that decreased KOR expression may be a homeostatic response to higher basal activation of KORs by MD-induced increases in DYN and p38 expression in the LHb. Ih- and Ih+ neurons molecular identification is further investigated following electrophysiological recordings by combining ionophoresis labeling during electrophysiology experiments and post-hoc immunohistochemistry to verify the glutamatergic (VGLUT+) and GABAergic (somatostatin, SST+ or parvalbumin, PV) nature of LHb neurons. Collectively, our current studies provide evidence for input- and cell-specific regulation of LHb neurons by KOR signaling that could be modulated by MD and highlights a future hotspot for pharmacological intervention of KOR-mediated mood disorders through dysregulation of specific LHb neural circuits.

Value-Based Decision to Redevelop Transportation Facilities: A Case Study of an Abandoned Airport

Value-based decision making is the consideration of function and cost in the decision to select the highest possible option. Reviews to previous studies showed that there is a lack of consideration of the criteria of value in the decision-making process. Hence, this research aims to identify the criteria for redeveloping transportation facilities based on function and cost. The ultimate goal of this research is to select the best value alternative for project redevelopment. An abandoned airport was chosen as the case study to evaluate the decision made by the government to invest in the project of transforming the abandoned airport into a hospital that aims to cater to the increasing demand for healthcare. A quantitative and qualitative approach was applied through literature review, survey, and interview to experts in the area of transportation facilities development. Statistical descriptive analysis was used to analyse the first survey, which was to investigate the critical factors under the category of function. The second survey was conducted to select the most valuable alternative for the redevelopment by utilising the Analytical Hierarchy Process (AHP), and it was found that the transportation hub is the most valuable alternative for redeveloping the abandoned airport. The result was then validated by an expert from the industry, and it can be concluded that the value-based decision-making model can be used in enhancing the decision of the redevelopment of transportation facilities by considering both function and cost.

Trading mental effort for confidence in the metacognitive control of value-based decision-making.

Why do we sometimes opt for actions or items that we do not value the most? Under current neurocomputational theories, such preference reversals are typically interpreted in terms of errors that arise from the unreliable signaling of value to brain decision systems. But, an alternative explanation is that people may change their mind because they are reassessing the value of alternative options while pondering the decision. So, why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide initial (and largely uncertain) representations of options' values, yielding prior estimates of decision difficulty. These uncertain value representations are then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty. The ensuing MCD model predicts response time, subjective feeling of effort, choice confidence, changes of mind, as well as choice-induced preference change and certainty gain. We test these predictions in a systematic manner, using a dedicated behavioral paradigm. Our results provide a quantitative link between mental effort, choice confidence, and preference reversals, which could inform interpretations of related neuroimaging findings.

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data.

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

Climate policy decision making in contexts of deep uncertainty - from optimisation to robustness

Integrated assessment models are often used to evaluate the role of different technologies in meeting global climate goals. Such models have been criticised for failing to address the deep uncertainties and plurality of values that are fundamental to energy transitions. One consequence is that model scenarios overwhelmingly depend on large-scale carbon dioxide removal to hold warming to below 2 °C.Here we propose an alternative approach using Scenario-Focused Decision Analysis (SFDA) as methods that embrace decision making under deep uncertainty. SFDA can accommodate a range of value sets and perspectives, and most importantly can integrate value-based decision-making in designing climate policy. We specifically consider Robust Decision Making (RDM) as an exemplar of SFDA for developing climate policy.We outline an iterative five-stage framework for RDM using the role of carbon dioxide removal in long-term mitigation pathways as an example. The five steps comprise (i) participatory definition of goals, values, potential policy options and uncertainties; (ii) modelling the performance of policy portfolios across a wide range of future scenarios; (iii) visualisation and identification of portfolio vulnerabilities; (iv) analysis of trade-offs; and (v) development of policy strategies. SFDA, and specifically RDM, provide untapped opportunities for diverse actors to explore alternative mitigation pathways and evaluate the robustness of climate policy choices through “deliberation with analysis”. In relation to carbon dioxide removal methods, RDM provides a framework for evaluating their potential for safely meeting climate goals in a societally acceptable manner.