Quality versus mere popularity: a conceptual map for understanding human behavior

Bentley, R. Alexander; O’Brien, Michael J.; Ormerod, Paul

doi:10.1007/s11299-011-0087-4

Cited by 13 publications

(16 citation statements)

References 51 publications

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“…Bentley et al [25,33] describe generalized data patterns that help to locate a phenomenon on the map in figure 1, given time-series data on the relative popularities of different options. The aim is to extract as much information as possible from patterns, such as the distribution of popularity for a single time phase, or ranked popularity lists and/or time series in the popularity of each option over a number of successive time phases.…”

Section: (B) Generalized Data Patternsmentioning

confidence: 99%

“…Social influence may or may not have increased with more participation, and decisions may have become (i) more transparent as the NHS communicated cost/ benefit information about screenings, or (ii) less transparent with the increase in options (and associated experts). Ideally, we would like to have popularity data on each option over the course of the programme, at finely resolved time intervals [25,33], such as monthly or quarterly data on the number of STI tests done, for each of the half-dozen options, over a decade and subdivided by gender, county, ethnicity and the like. In reality, the data available in this case include the popularity of the different choices, by gender, for two time periods Even with these numbers, we can find that the distribution was probably lognormal, which is a type of long-tailed probability-distribution function that we expect in the eastern part of the map, where social influence contributes to the multiplicative growth, or proportional advantage, in the popularity of choices.…”

Section: (B) Generalized Data Patternsmentioning

confidence: 99%

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Evaluating reproductive decisions as discrete choices under social influence

Bentley

Brock

Caiado

et al. 2016

Phil. Trans. R. Soc. B

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Discrete choice, coupled with social influence, plays a significant role in evolutionary studies of human fertility, as investigators explore how and why reproductive decisions are made. We have previously proposed that the relative magnitude of social influence can be compared against the transparency of pay-off, also known as the transparency of a decision, through a heuristic diagram that maps decision-making along two axes. The horizontal axis represents the degree to which an agent makes a decision individually versus one that is socially influenced, and the vertical axis represents the degree to which there is transparency in the pay-offs and risks associated with the decision the agent makes. Having previously parametrized the functions that underlie the diagram, we detail here how our estimation methods can be applied to real-world datasets concerning sexual health and contraception.

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Section: (B) Generalized Data Patternsmentioning

confidence: 99%

Section: (B) Generalized Data Patternsmentioning

confidence: 99%

Evaluating reproductive decisions as discrete choices under social influence

Bentley

Brock

Caiado

et al. 2016

Phil. Trans. R. Soc. B

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“…With the anthropologists Alex Bentley and Mike O'Brien, I published an initial attempt to make such a classification [4]. The underlying arguments can be summarised diagrammatically in the form of a '4-box', beloved of management consultants the world over.…”

Section: The Core Model Of Behaviourmentioning

confidence: 99%

“…4 The long-run expectation of a firm at any point in time is not the result of a rational calculation of the amount of profit which an investment is expected to yield. Rather it is a sentiment, the degree of optimism or pessimism which the agent holds about the future.…”

Section: Networkmentioning

confidence: 99%

Economics

Ormerod¹

2017

Understanding Complex Systems

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Economics is by no means an empty box. For example, it offers what is possibly the only general law of behaviour in the social sciences, namely that agents react to incentives. Over the past two decades or so, at the micro level, the level of individual agent decision making, the discipline has made progress. Developments in the econometric theory of the analysis of large longitudinal data bases and the rise of behavioural and experimental economics have made the discipline much more empirical. However, at the macro level, economics has, if anything, gone backwards. The main intellectual effort since the 1980s has been to import the concept of equilibrium into macroeconomics. It is no surprise that policymakers during the financial crisis of the late 2000s found the mainstream economic models to be of little or no help at all. In the 1950s, there was an active debate about the computational limits which agents faced when making decisions. The polymath Nobel Laureate Herbert Simon was prominent in arguing that the rational model of choice, the core model of economic theory, was not realistic in many situations because of these limits. Even after the event, it may not be possible to determine what the optimal decision would have been at any given time. The world is in general too complex. Mainstream economics gradually lost sight of this fundamental challenge to one of its key assumptions. The rise of cyber society and Big Data mean that Simon's challenge is more relevant than ever. Looking to the future, new models of 'rational' agent behaviour are required which are better suited to the cyber society of the twenty-first century. Key areas of research include: agent decision making rules; heuristics to identify decision types in any given context; network percolation of imitation, incentives, ideas, beliefs, influence and sentiments; networks evolution; the policy implications of different modes of behaviour; fundamental theory and tools to operationalise narrative dynamics; computational theories of narratives, including Big Data; tools for processing narratives and sentiment; and predicting the emergence of narratives. P. Ormerod ( )

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“…A classifying heuristic Bentley, O'Brien, Ormerod (2011) develop a heuristic for classifying the circumstances in which different kinds of models are the relevant 'null' models of behaviour with which to account for how agents select amongst alternatives. To emphasise, it is a heuristic and not in any way intended to be a complete set of criteria for such classification.…”

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confidence: 99%

The Economics of Radical Uncertainty

Ormerod

2015

Economics

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In situations of what we now describe as radical uncertainty, the core model of agent behaviour, of rational autonomous agents with stable preferences, is not useful. Instead, a different principle, in which the decisions of an agent are based directly on the decisions and strategies of other agents, is offered as a relevant core model. Preferences are not stable, but evolve. It is not a special case in such circumstances, but the general one. The author provides empirical evidence to suggest that as a description of behaviour in the modern world, economic rationality is applicable in a declining number of situations. He discusses models drawn from the modern literature on cultural evolution in which imitation of others is the basic strategy, and suggests a heuristic way of classifying situations in which the different models are relevant. The key point is that in situations where radical uncertainty is present, we require theoretical 'null' models of agent behaviour which are different from those of economic rationality. Under uncertainty, fundamentally different behavioural rules are 'rational'. The author gives an example of a very simple pure sentiment model of the business cycle, in which agents use very simple heuristic decision rules. It is nevertheless capable of approximating a number of deep features of output growth over the cycle.

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Quality versus mere popularity: a conceptual map for understanding human behavior

Abstract: Cultural transmission, Evolution, Pattern formation, Popularity, Social learning,

Cited by 13 publications

References 51 publications

Evaluating reproductive decisions as discrete choices under social influence

Evaluating reproductive decisions as discrete choices under social influence

Economics

The Economics of Radical Uncertainty

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