Academic literature on the topic 'Behavioural biases'

The behavioural revolution has profoundly affected how we conceptualize behaviour. The rational agent of standard microeconomic theory has been found wanting and, in its place, new formulations have been presented which take seriously human traits like myopia and loss aversion. Here it is argued that the behavioural revolution offers a way of understanding common problems in economic geography, such as co-location, clusters of innovation, the diffusion of innovation, and home bias. It is noted that earlier versions of behaviouralism stressed bounded rationality but underestimated the far-reaching consequences of the behavioural revolution. To explain the significance of these developments for understanding the intersection between cognition and context, we look closely at behaviour in time and space. The implications of behaviouralism for institutions are briefly considered, emphasizing the role that collective action in or through institutions can play in ameliorating the adverse effects of behavioural biases and anomalies.

This chapter considers how dynamic brain states continuously fine-tune processing to accommodate changes in behavioural context and task goals. First, the authors review the extant literature suggesting that content-specific patterns of preparatory activity bias competitive processing in visual cortex to favour behaviourally relevant input. Next, they consider how higher-level brain areas might provide a top-down attentional signal for modulating baseline visual activity. Extensive evidence suggests that working memory representations in prefrontal cortex are especially important for generating and maintaining biases in preparatory visual activity via modulatory feedback. Although it is often proposed that such working memory representations are maintained via persistent prefrontal activity, the authors review more recent evidence that rapid short-term synaptic plasticity provides a common substrate for maintaining the content of past experience and the rules for guiding future goal-directed processing.

This chapter explores what the reasons for deviating from modified universalism in practice may be. To do so, it draws on behavioural international law and economics. The chapter argues that certain decision-making biases may play a role in cross-border insolvency and can explain both negative inclinations and instances of lack of cooperation, as well as the relative success of modified universalism. The key argument here is that instead of yielding to territorial inclinations, cross-border insolvency law has a debiasing role to play. It should attempt to align choices with optimal solutions, overcoming biases, and should also close gaps in the cross-border insolvency system in line with modified universalism.

This book interrogates the current cross-border insolvency regime and sets out a pattern to improve its future. In recent decades, and especially since the global financial crisis, a number of important initiatives have focused on developing effective solutions for managing the insolvency of multinational enterprises and financial institutions. This book takes stock of the varying success of previous policy, and identifies the gaps and biases that could be bridged by employing a range of strategies. The book first sets out the theoretical debates regarding cross-border insolvency and surveys the strengths and weaknesses of the prevailing method, ‘modified universalism’, synthesizing divergences into a rubric for both commercial entities and financial institutions. Adhering to these norms more robustly, the book argues, would enhance global welfare and produce the best outcomes for businesses and institutions. Drawing upon sources from international law as well as behavioural and economic theory, the book considers how to translate modified universalism into binding international law, how to choose the right instrument for cross-border insolvency, the impact instrument design has on decisions and choices, and the means to encourage compliance. In particular, the book proposes measures that could potentially overcome, or at least take into account, behavioural biases in decision-making in order to create a system that works for businesses, and offers a blueprint for the future of cross-border insolvency.

Propaganda was historically a neutral descriptive term; it was used by playwrights in ancient Greece to express the act of promoting political and religious beliefs. In the 19th century, with the birth of the press, cinema and radio, propaganda transformed into a tool destined to shape people�s beliefs and behaviours. It is generally biased, misleading, or even false to promote a specific agenda or point of view. Especially in Europe, in the first half of the 20th century, propaganda was used by European dictatorial regimes: Nazism, Fascism and Communism for popular consensus building and political repression; and it�s still widely used nowadays for the same proposition by current autocratic regimes around the world. The main purpose of this article is to demonstrate how Kremlin propaganda is used thanks to modern mass communication methods and technologies to influence the social and moral cognition of Russian society to start and continue the 10 year-long war against Ukraine, all now in progress. In this study we demonstrate how the Russian propaganda also as one of main causes of war itself. Because to start a war it is necessary to have first the consent of one�s own people who accept, support and justify the invasion of another country and here the main role is played by propaganda of Putin�s political regime. Well-thought-out Kremlin�s propaganda has constantly incited different fears in individuals through using persuasive neurolinguistic techniques: in information texts, in social networks, and especially on TV; by doing so over the past 20 years has turned at least 80% of Russian society into people who exist in a parallel reality and seem to have no critical thinking. The phenomenon of propaganda and its social and moral damage is analysed using quantitative, qualitative, analytical and descriptive research methods. Furthermore, thanks to this research, we are trying to understand if and how it will be possible to effectively counter the Kremlin�s propaganda machine. The paper�s results suggest communicative and social tools that could change public opinion in Russia and spread the truth about the war. Unfortunately, this would only be possible after the end of the war and the transition of the Kremlin dictatorial regime to a democratic one. Future research may sometimes attempt to identify the most effective means and strategies to achieve this goal, hopefully to be able to implement these soon.

As almost every major problem that humankind faces is a consequence of human behaviour, predicting behaviour and behaviour change is fundamental. Given the multitude of factors that affect our decision making, a transdisciplinary understanding of behaviour is impossible without the integration of data that crosses disciplinary boundaries. The concept of Conduct-“ome” is an analog of those holistic –“omic”-approaches found in the biological sciences which take a “totality of factors” approach, and provides a framework for studying human behaviour in a multifactorial, multidisciplinary context, accounting for a wealth of potential causes of behaviour, from the genetic and epigenetic to psychological, neurological, social, physiological, clinical, socio-economic, socio-demographic, socio-political and ethical factors. Conductome, as opposed to behaviour-ome, is used, as it directly addresses the “whys” (causes) of the considered behaviour. We argue that behaviour can only be understood probabilistically, through a process of statistical inference that constructs P(A|X), the probability for a conduct A conditioned on the large set of factors, X, that predict it. This inference process can be based on an “external” ensemble of objective, countable events, using a frequentist interpretation of probabilities, or on an “internal” ensemble, implicit in our mental models and based on a Bayesian interpretation. Including both these approaches allows one to compare objective, observable reality with the subjective perception of reality constructed within a mental model, allowing for the identification of discrepancies between the two in the form of cognitive biases. A key component for constructing the Conductome is the obtention of data that transcends disciplines that can be used to link a range of relevant behaviours as internal and external effects to their causes. A second component is the use of advanced modelling tools, such as machine learning, for the analysis of such multi-scale data and the construction of explicit prediction models for a given conduct. In this article, the feasibility of the Conductome approach is illustrated by considering obesity-related behaviours; as obesity has become one of the key social problems that affects a growing segment of the population worldwide. In summary: The objective is to understand, interpret and provide an interdisciplinary, computational, and data-based framework for generating prediction models for addressing problems that originate in human behaviour.

The Capacity Development Forum (CDF) is an initiative of the Indian Institute for Human Settlements (IIHS) that aims to bring together diverse stakeholders involved in capacity development in India into a ‘community of practice’ to consolidate learnings from across the country and around the world into a strong and value-added network to consolidate learnings across the country and from around the world. The longer-term objective of the forum is to collaborate on Capacity Development practices and create a repository of knowledge on Capacity Development. The third convening of the IIHS Capacity Development Forum was held on 27-28 June 2024 in a hybrid format – at IIHS, Bengaluru City Campus and online, on the theme: ‘Capacity Development and Behavioural Insights – Implications for Institutional Design and Public Policy’. The CDF 2024 focussed on how the understanding of human behaviour and motivations is key to achieving larger objectives not only at an individual level, but also within institutions and societies. The panels brought together stakeholders from a range of sectors to discuss how nudges and incentives can be used to influence human behaviour at various scales, and how this in turn informs the design, formulation and implementation of policies and programmes to make them more effective. Day 1 of the convening comprised panels that brought together capacity development practitioners from the public and private sectors, to discuss various aspects of policy capacity and enhancing it through incorporating behavioural insights, international perspectives on the role of Capacity Development agencies in developing behavioural science capacity of public officials, and the role of leadership in organisational behaviour change. This was followed by a learning hub on Day 2 focussing on cases from practice incorporating behavioural insights from different sectors, and a game-based learning workshop incorporating behavioural insights and illustrating various biases in human behaviour.

Annuities, long-term care insurance and reverse mortgages remain unpopular to manage longevity, medical and housing price risks after retirement. We analyze low demand using a life-cycle model structurally estimated with a unique stated-preference survey experiment of Canadian households. Low risk aversion, substitution between housing and consumption and low marginal utility when in poor health explain most of the reduced demand. Bequests motives are found to be a luxury good and play a limited role. The remaining disinterest is explained by information frictions and behavioural status-quo biases. We find evidence of strong spousal co-insurance motives motivating LTCI and of responsiveness to bundling with a near doubling of demand for annuities when reverse mortgages can be used to annuitize, instead of consuming home equity

The rise in inflation in recent years triggered a cost-of-living crisis, in which households’ ability to afford basic necessities declined. The cost-of-living crisis disproportionately affected lower income households, with the proportion in material deprivation (i.e. unable to afford basic necessities) rising by a fifth. This study examines how these households responded to this economic pressure and the challenges they faced in accessing government benefits and available support services during the cost-of-living crisis. In the absence of detailed longitudinal data, the study employed a cross-sectional survey with techniques from behavioural science to improve recall and limit biases in responses. A nationally representative sample of 1,615 financial decision-makers from low- (i.e. below ~85 per cent of the median) income households undertook the study between May and June 2024. Most were recruited from the online panels of market research companies and completed the study online, but a subset was recruited in-person in low-income areas. Participants reported on changes they made in their day-to-day spending, borrowing and saving due to the cost-of-living crisis, described as the period following the Russian invasion of Ukraine in 2022. They also reported on their use of government benefits and support services during the cost-of-living crisis, with a focus on administrative burdens (i.e. the time, effort, and frustration involved and their potential role as barriers to using supports).

The purpose of this project was to review and utilise methods from other disciplines in the social sciences in order to be able make stronger causal claims using crosssectional gambling data such as gambling prevalence studies. We focused on the question of whether there is a causal relationship between specific gambling products and individual gambling harms, specifically problem gambling. There has been an existing literature that has looked at this issue, but fails to control for selection biases on engagement with specific gambling behaviours. We reviewed and used three approaches: propensity score matching, coarsened exact matching, and sample selection modelling to account for this limitation. Then, we applied them to examine the relationship between 8 types of gambling activity (online betting, offline betting, pools, scratchcards, lotteries, pools, offline bingo, and slot machines/FOBTs) on problem gambling in 9 British gambling prevalence surveys: the Health Survey for England in 2012, 2015, 2016 and 2016; the Scottish Health Survey in 2012, 2015, and 2016, and the British Gambling Prevalence Study in 2007 and 2010. The results showed most gambling activities were, unsurprisingly, associated with an increased risk of addictive behaviour. However, there was a clear gradient of risk. For some gambling activities (i.e., online gambling, slots), the effect sizes were substantially higher than others (i.e., lotteries). The modelling also highlighted covariates that exert strong effects on both the IV and DV of interest at the same time, such as age. The findings of this project thus highlight the importance of controlling for selection mechanisms that influence both engagement and outcomes of interest. There are some activities (e.g., bingo, pools) where these substantially affect relationships between engagement and harms. Nonetheless, there is clear evidence of associations between certain types of gambling product and indicators of harm when these are controlled for.

To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristics as race, gender and class. Second, among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use. Third, bias in military AI can have various humanitarian consequences depending on context and use. These range from misidentifying people and objects in targeting decisions to generating flawed assessments of humanitarian needs.

Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.

This working paper surveys impact evaluations of government Technology Development Funds (TDFs) in Argentina, Brazil, Chile and Panama. All the evaluations were done at the recipient (firm) level using data from innovation surveys, industrial surveys, and administrative records of the granting units, together with quasi-experimental econometric techniques to minimize the effects of any selection bias. The surveyed evaluations considered four levels of potential impact: R&D input additionality, behavioural additionality, increases in innovative output, and improvements in performance. The evidence suggests that TDFs do not crowd out private investment and that they positively affect R&D intensity. In addition, participation in TDFs induces a more proactive attitude of beneficiary firms towards innovation activities.

Executive Summary This report describes a series of experiments carried out by PRICE Lab, a research programme at the Economic and Social Research Institute (ESRI) jointly funded by the Central Bank of Ireland, the Commission for Energy Regulation, the Competition and Consumer Protection Commission and the Commission for Communications Regulation. The experiments were conducted with samples of Irish consumers aged 18-70 years and were designed to answer the following general research question: At what point do products become too complex for consumers to choose accurately between the good ones and the bad ones? BACKGROUND AND METHODS PRICE Lab represents a departure from traditional methods employed for economic research in Ireland. It belongs to the rapidly expanding area of ‘behavioural economics’, which is the application of psychological insights to economic analysis. In recent years, behavioural economics has developed novel methods and generated many new findings, especially in relation to the choices made by consumers. These scientific advances have implications both for economics and for policy. They suggest that consumers often do not make decisions in the way that economists have traditionally assumed. The findings show that consumers have limited capacity for attending to and processing information and that they are prone to systematic biases, all of which may lead to disadvantageous choices. In short, consumers may make costly mistakes. Research has indeed documented that in several key consumer markets, including financial services, utilities and telecommunications, many consumers struggle to choose the best products for themselves. It is often argued that these markets involve ‘complex’ products. The obvious question that arises is whether consumer policy can be used to help them to make better choices when faced with complex products. Policies are more likely to be successful where they are informed by an accurate understanding of how real consumers make decisions between products. To provide evidence for consumer policy, PRICE Lab has developed a method for measuring the accuracy with which consumers make choices, using techniques adapted from the scientific study of human perception. The method allows researchers to measure how reliably consumers can distinguish a good deal from a bad one. A good deal is defined here as one where the product is more valuable than the price paid. In other words, it offers good value for money or, in the jargon of economics, offers the consumer a ‘surplus’. Conversely, a bad deal offers poor value for money, providing no (or a negative) surplus. PRICE Lab’s main experimental method, which we call the ‘Surplus Identification’ (S-ID) task, allows researchers to measure how accurately consumers can spot a surplus and whether they are prone to systematic biases. Most importantly, the S-ID task can be used to study how the accuracy of consumers’ decisions changes as the type of product changes. For the experiments we report here, samples of consumers arrived at the ESRI one at a time and spent approximately one hour doing the S-ID task with different kinds of products, which were displayed on a computer screen. They had to learn to judge the value of one or more products against prices and were then tested for accuracy. As well as people’s intrinsic motivation to do well when their performance on a task like this is tested, we provided an incentive: one in every ten consumers who attended PRICE Lab won a prize, based on their performance. Across a series of these experiments, we were able to test how the accuracy of consumers’ decisions was affected by the number and nature of the product’s characteristics, or ‘attributes’, which they had to take into account in order to distinguish good deals from bad ones. In other words, we were able to study what exactly makes for a ‘complex’ product, in the sense that consumers find it difficult to choose good deals. FINDINGS Overall, across all ten experiments described in this report, we found that consumers’ judgements of the value of products against prices were surprisingly inaccurate. Even when the product was simple, meaning that it consisted of just one clearly perceptible attribute (e.g. the product was worth more when it was larger), consumers required a surplus of around 16-26 per cent of the total price range in order to be able to judge accurately that a deal was a good one rather than a bad one. Put another way, when most people have to map a characteristic of a product onto a range of prices, they are able to distinguish at best between five and seven levels of value (e.g. five levels might be thought of as equivalent to ‘very bad’, ‘bad’, ‘average’, ‘good’, ‘very good’). Furthermore, we found that judgements of products against prices were not only imprecise, but systematically biased. Consumers generally overestimated what products at the top end of the range were worth and underestimated what products at the bottom end of the range were worth, typically by as much as 10-15 per cent and sometimes more. We then systematically increased the complexity of the products, first by adding more attributes, so that the consumers had to take into account, two, three, then four different characteristics of the product simultaneously. One product might be good on attribute A, not so good on attribute B and available at just above the xii | PRICE Lab: An Investigation of Consumers’ Capabilities with Complex Products average price; another might be very good on A, middling on B, but relatively expensive. Each time the consumer’s task was to judge whether the deal was good or bad. We would then add complexity by introducing attribute C, then attribute D, and so on. Thus, consumers had to negotiate multiple trade-offs. Performance deteriorated quite rapidly once multiple attributes were in play. Even the best performers could not integrate all of the product information efficiently – they became substantially more likely to make mistakes. Once people had to consider four product characteristics simultaneously, all of which contributed equally to the monetary value of the product, a surplus of more than half the price range was required for them to identify a good deal reliably. This was a fundamental finding of the present experiments: once consumers had to take into account more than two or three different factors simultaneously their ability to distinguish good and bad deals became strikingly imprecise. This finding therefore offered a clear answer to our primary research question: a product might be considered ‘complex’ once consumers must take into account more than two or three factors simultaneously in order to judge whether a deal is good or bad. Most of the experiments conducted after we obtained these strong initial findings were designed to test whether consumers could improve on this level of performance, perhaps for certain types of products or with sufficient practice, or whether the performance limits uncovered were likely to apply across many different types of product. An examination of individual differences revealed that some people were significantly better than others at judging good deals from bad ones. However the differences were not large in comparison to the overall effects recorded; everyone tested struggled once there were more than two or three product attributes to contend with. People with high levels of numeracy and educational attainment performed slightly better than those without, but the improvement was small. We also found that both the high level of imprecision and systematic bias were not reduced substantially by giving people substantial practice and opportunities to learn – any improvements were slow and incremental. A series of experiments was also designed to test whether consumers’ capability was different depending on the type of product attribute. In our initial experiments the characteristics of the products were all visual (e.g., size, fineness of texture, etc.). We then performed similar experiments where the relevant product information was supplied as numbers (e.g., percentages, amounts) or in categories (e.g., Type A, Rating D, Brand X), to see whether performance might improve. This question is important, as most financial and contractual information is supplied to consumers in a numeric or categorical form. The results showed clearly that the type of product information did not matter for the level of imprecision and bias in consumers’ decisions – the results were essentially the same whether the product attributes were visual, numeric or categorical. What continued to drive performance was how many characteristics the consumer had to judge simultaneously. Thus, our findings were not the result of people failing to perceive or take in information accurately. Rather, the limiting factor in consumers’ capability was how many different factors they had to weigh against each other at the same time. In most of our experiments the characteristics of the product and its monetary value were related by a one-to-one mapping; each extra unit of an attribute added the same amount of monetary value. In other words, the relationships were all linear. Because other findings in behavioural economics suggest that consumers might struggle more with non-linear relationships, we designed experiments to test them. For example, the monetary value of a product might increase more when the amount of one attribute moves from very low to low, than when it moves from high to very high. We found that this made no difference to either the imprecision or bias in consumers’ decisions provided that the relationship was monotonic (i.e. the direction of the relationship was consistent, so that more or less of the attribute always meant more or less monetary value respectively). When the relationship involved a turning point (i.e. more of the attribute meant higher monetary value but only up to a certain point, after which more of the attribute meant less value) consumers’ judgements were more imprecise still. Finally, we tested whether familiarity with the type of product improved performance. In most of the experiments we intentionally used products that were new to the experimental participants. This was done to ensure experimental control and so that we could monitor learning. In the final experiment reported here, we used two familiar products (Dublin houses and residential broadband packages) and tested whether consumers could distinguish good deals from bad deals any better among these familiar products than they could among products that they had never seen before, but which had the same number and type of attributes and price range. We found that consumers’ performance was the same for these familiar products as for unfamiliar ones. Again, what primarily determined the amount of imprecision and bias in consumers’ judgments was the number of attributes that they had to balance against each other, regardless of whether these were familiar or novel. POLICY IMPLICATIONS There is a menu of consumer polices designed to assist consumers in negotiating complex products. A review, including international examples, is given in the main body of the report. The primary aim is often to simplify the consumer’s task. Potential policies, versions of which already exist in various forms and which cover a spectrum of interventionist strength, might include: the provision and endorsement of independent, transparent price comparison websites and other choice engines (e.g. mobile applications, decision software); the provision of high quality independent consumer advice; ‘mandated simplification’, whereby regulations stipulate that providers must present product information in a simplified and standardised format specifically determined by regulation; and more strident interventions such as devising and enforcing prescriptive rules and regulations in relation to permissible product descriptions, product features or price structures. The present findings have implications for such policies. However, while the experimental findings have implications for policy, it needs to be borne in mind that the evidence supplied here is only one factor in determining whether any given intervention in markets is likely to be beneficial. The findings imply that consumers are likely to struggle to choose well in markets with products consisting of multiple important attributes that must all be factored in when making a choice. Interventions that reduce this kind of complexity for consumers may therefore be beneficial, but nothing in the present research addresses the potential costs of such interventions, or how providers are likely to respond to them. The findings are also general in nature and are intended to give insights into consumer choices across markets. There are likely to be additional factors specific to certain markets that need to be considered in any analysis of the costs and benefits of a potential policy change. Most importantly, the policy implications discussed here are not specific to Ireland or to any particular product market. Furthermore, they should not be read as criticisms of existing regulatory regimes, which already go to some lengths in assisting consumers to deal with complex products. Ireland currently has extensive regulations designed to protect consumers, both in general and in specific markets, descriptions of which can be found in Section 9.1 of the main report. Nevertheless, the experiments described here do offer relevant guidance for future policy designs. For instance, they imply that while policies that make it easier for consumers to switch providers may be necessary to encourage active consumers, they may not be sufficient, especially in markets where products are complex. In order for consumers to benefit, policies that help them to identify better deals reliably may also be required, given the scale of inaccuracy in consumers’ decisions that we record in this report when products have multiple important attributes. Where policies are designed to assist consumer decisions, the present findings imply quite severe limits in relation to the volume of information consumers can simultaneously take into account. Good impartial Executive Summary | xv consumer advice may limit the volume of information and focus on ensuring that the most important product attributes are recognised by consumers. The findings also have implications for the role of competition. While consumers may obtain substantial potential benefits from competition, their capabilities when faced with more complex products are likely to reduce such benefits. Pressure from competition requires sufficient numbers of consumers to spot and exploit better value offerings. Given our results, providers with larger market shares may face incentives to increase the complexity of products in an effort to dampen competitive pressure and generate more market power. Where marketing or pricing practices result in prices or attributes with multiple components, our findings imply that consumer choices are likely to become less accurate. Policymakers must of course be careful in determining whether such practices amount to legitimate innovations with potential consumer benefit. Yet there is a genuine danger that spurious complexity can be generated that confuses consumers and protects market power. The results described here provide backing for the promotion and/or provision by policymakers of high-quality independent choice engines, including but not limited to price comparison sites, especially in circumstances where the number of relevant product attributes is high. A longer discussion of the potential benefits and caveats associated with such policies is contained in the main body of the report. Mandated simplification policies are gaining in popularity internationally. Examples include limiting the number of tariffs a single energy company can offer or standardising health insurance products, both of which are designed to simplify the comparisons between prices and/or product attributes. The present research has some implications for what might make a good mandate. Consumer decisions are likely to be improved where a mandate brings to the consumer’s attention the most important product attributes at the point of decision. The present results offer guidance with respect to how many key attributes consumers are able simultaneously to trade off, with implications for the design of standardised disclosures. While bearing in mind the potential for imposing costs, the results also suggest benefits to compulsory ‘meta-attributes’ (such as APRs, energy ratings, total costs, etc.), which may help consumers to integrate otherwise separate sources of information. FUTURE RESEARCH The experiments described here were designed to produce findings that generalise across multiple product markets. However, in addition to the results outlined in this report, the work has resulted in new experimental methods that can be applied to more specific consumer policy issues. This is possible because the methods generate experimental measures of the accuracy of consumers’ decision-making. As such, they can be adapted to assess the quality of consumers’ decisions in relation to specific products, pricing and marketing practices. Work is underway in PRICE Lab that applies these methods to issues in specific markets, including those for personal loans, energy and mobile phones.

There is anecdotal evidence that the rate of complaints and claims against doctors is rising, yet little is known about (Q1) which types of patients are more likely to make a complaint or claim, nor about (Q2) what interventions are effective in reducing rates of complaints and claims and increasing patient satisfaction. This Evidence Check aimed to answer those two questions. The evidence base for both of the questions was of low quality, with only five studies having a comparison group. Twenty-five studies addressed Question 1. The only patient characteristic to have a consistent effect on rates of complaints and claims was having a mental, behavioural or developmental disorder. Other patient characteristics, including those related to their therapeutic context, had inconsistent or weak relationships with rates of complaints and claims. Twenty studies addressed Question 2 (including one which addressed both questions). There were consistently reduced rates of complaints and claims following implementation of risk management programs and also implementation of communication and resolution programs. Peer feedback programs consistently improved doctors’ response to complaints and subsequent performance. However, the results found here should be interpreted with caution, as the risk of bias inherent in the study designs makes them more likely to erroneously demonstrate an effect.