How can neuroscience inform economics?

Krajbich, Ian; Dean, Mark

doi:10.1016/j.cobeha.2015.07.005

Cited by 30 publications

(19 citation statements)

References 53 publications

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“…The use of timing differences as described above would be consistent with at least two existing theories on the role of attention in cognition. First, it is consistent with the idea that rational inattention strategies [63][64][65] can be employed as a means of reducing effort costs. Specifically, if the time advantage for healthiness is large enough, then one could theoretically decide against eating an unhealthy food before even considering its tastiness and thus not experience temptation or conflict.…”

Section: Discussionsupporting

confidence: 76%

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Maier

Beharelle

Polanía

et al. 2018

Preprint

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Rewards usually have multiple attributes that are relevant for behaviour. For instance, even apparently simple choices between liquid or food rewards involve comparisons of at least two attributes, flavour and amount. Thus, in order to make the best choice, an organism will need to take multiple attributes into account. Theories and models of decision making usually focus on how strongly different attributes are weighted in choice, e.g., as a function of their importance or salience to the decision-maker. However, when different attributes impact on the decision process is a question that has received far less attention. Although one may intuitively assume a systematic relationship between the weighting strength and the timing with which different attributes impact on the final choice, this relationship is untested. Here, we investigate whether attribute timing has a unique influence on decision making using a time-varying drift diffusion model (tDDM) and data from four separate experiments. Contrary to expectations, we find only a modest correlation between how strongly and how quickly reward attributes impact on choice. Experimental manipulations of attention and neural activity demonstrate that we can dissociate at the cognitive and neural levels the processes that determine the relative weighting strength and timing of attribute consideration. Our findings demonstrate that processes determining either the weighting strengths or the timing of attributes in decision making can adapt independently to changes in the environment or goals. Moreover, they show that a tDDM incorporating separable influences of these two sets of processes on choice improves understanding and predictions of individual differences in decision behaviour. While if healthiness enters first it isEq.Thus, the times at which the weighted value differences in tastiness and healthiness attributes (ω taste *TD and ω health *HD, respectively) begin to influence the evidence accumulation rate are determined by !"#. When the conditional statement ! > | !"# !" | is false, it equals 0, while if true it equals 1. Multiplying one of the two weighted attribute values by 0 until ! > | !"# !" | is true means that that attribute does not factor into the evidence accumulation process for the initial time period Figure 2. Patterns of behaviour resulting from asynchronous evidence accumulation. (a) In this diagram of the drift-diffusion evidence accumulation process, we have assigned the tastier choice to the upper boundary (+1), the healthier choice to the lower boundary (- 1), the starting point bias to zero, and the non-decision time to 0.7 s. The coloured lines indicate several example food choice response times from two distinct types of people. The traces that are initially coloured red are simulations of a decision-maker who considers tastiness alone for a period of time before beginning to consider healthiness. The traces that are initially blue are simulations from a decision-maker who considers healthiness alone for that same initial time window. Th...

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Section: Discussionsupporting

confidence: 76%

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Maier

Beharelle

Polanía

et al. 2018

Preprint

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“…It has also been demonstrated that context effects, which DFT predicts efficiently, may be fundamental to decision making [Trueblood et al, 2013], with similarity, attraction and compromise effects all appearing in a perceptual decision task. Whilst there has not yet been a large impact from neuroscience on economics [Krajbich and Dean, 2015], Busemeyer et al [2006] suggest that the accumulation of preference, as modelled by the behaviourally derived diffusion models in DFT, closely mimics neural activations in non-human primates during perceptual decision-making tasks. For example, Gold and Shadlen [2000] found evidence of an accumulating balance of sensory information favouring one interpretation over another in the neural circuits that generate and inform a monkey's choice.…”

Section: Arguments In Favour Of Decision Field Theorymentioning

confidence: 99%

Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques

Hancock

Hess

Choudhury

2018

Transportation Research Part B: Methodological

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There is a growing interest in the travel behaviour modelling community in using alternative methods to capture the behavioural mechanisms that drive our transport choices. The traditional method has been Random Utility Maximisation (RUM) and recent interest has focussed on Random Regret Minimisation (RRM), but there are many other possibilities. Decision Field Theory (DFT), a dynamic model popular in mathematical psychology, has recently been put forward as a rival to RUM but has not yet been investigated in detail or compared against other competing models like RRM. This paper considers arguments in favour of using DFT, reviews how it has been used in transport literature so far and provides theoretical improvements to further the mechanisms behind DFT to better represent general decision making. In particular, we demonstrate how the probability of alternatives can be calculated after any number of timesteps in a DFT model. We then look at how to best operationalise DFT using simulated datasets, finding that it can cope with underlying preferences towards alternatives, can include socio-demographic variables and that it performs best when standard score normalisation is applied to the alternative attribute levels. We also present a detailed comparison of DFT and Multinomial Logit (MNL) models using stated preference route choice datasets and find that DFT achieves significantly better fit in estimation as well as forecasting. We also find that our theoretical improvement provides DFT with much greater flexibility and that there are numerous approaches that can be adopted to incorporate heterogeneity within a DFT model. In particular, random parameters vastly improve the model fit.

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“…This paper presents a novel biological perspective from the scientific domain of neuroscience in order to understand the challenges managers face when engaging in sustainability management. Its purpose is to introduce insights from the growing discipline of neuroscience to address the question: “Are there neurological dynamics and propensities within the human brain that predispose and/or inhibit humans towards sustainable management?” While neuroscientific research has been used to advance theory and practice in a number of domains – economics (Krajbich & Dean, ), ethics (Meynen, ), information systems (vom Brocke & Liang, ), leadership (Waldman & Balthazard, ), management (McDonald & Tang, ) and marketing (Stipp, ) – research at the intersection of neuroscience and sustainability management has been absent.…”

Section: Introductionmentioning

confidence: 99%

Sustainability management: research insights from social cognitive neuroscience

McDonald

2018

Bus Strat Env

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Research in the field of social cognitive neuroscience is advancing rapidly, accelerated by technological innovation in brain imaging. This research is producing novel insights as to how humans process information, make decisions and behave in response to the growing imperatives for sustainability management. Recent research findings have implications for the theoretical foundations of sustainability management as well as the practical challenges confronting managers. Neuroscientific evidence underscores the magnitude of the challenges. Physiological evidence of six neural processes, hardwired via synaptic connections and potentially antagonistic to enhanced sustainability management, establish the structure of the paper. These neural phenomena, which are reflexive and preconscious, include: amygdala intercept, in‐group/out‐group differentiation, loss aversion, implicit persuasion and priming effects – cognitive and cultural. Propositions are advanced linking neurological processes to sustainability management. The paper concludes with strategic considerations and practical implications for greater integration between social cognitive neuroscience and sustainability management research.

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How can neuroscience inform economics?

Cited by 30 publications

References 53 publications

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques

Sustainability management: research insights from social cognitive neuroscience

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