During a session of the 46th session of the UN Statistical Commission on the the post-2015 development agenda, UN Deputy Secretary-General Jan Eliasson said data will be the “lifeblood of decision-making and the raw material for accountability” in the new agenda and called for a statistical framework that would meet such expectations (http://sd.iisd.org/news/un-statistical-commission-sets-roadmap-to-post-2015-indicators/). Statistics has always been presented as a support to decision making, whether is is official statistics or statistics collected for monitoring and evaluation purposes. There is here the implicit assumption that, if we provide the right statistical information to decision-makers they will make full use of it in order to make rational decisions. That's exactly what theories of bounded rationality are contesting: for various reasons people do not use fully rational problem-solving methodologies even in the presence of full information. This has been shown by many authors, among which the work of Herbert Simon is considered as seminal. As shown by further research, reasons for not using fully rational-problem solving methodology with full information include limited time or the high computational cost of fully rational methodologies.
Can a discipline that's objective is to provide information for better decision-making ignore well-known flaws in the use of that information for rational decisions? Of course no. Furthermore, the advent of big data is likely to increase one of the causes of heuristics biases: the overflow of information. If computer power does not follow the increase in the flow of information, people will never be able to analyze the massive amount of data and will necessarily rely on various heuristics using only part of the available information. For these reasons, integrating knowledge on bounded rationality can certainly help official statistics. If the ultimate objective of official statistics is better decision-making then providing bias-correcting information may be even more effective than producing big amount of data that will not be used in a fully rational manner.
Here are some questions related to bounded rationality and which answers can help improving official statistics, in particular in the era of big data:
There is no doubt that the key to some difficult issues in statistical development may be found in the study of the heuristics used by decisions-makers under 'bounded rationality'. It is even possible that some intractable problems in statistical development are just the result of the bounded rationality in decision-making and that the approaches to correcting these issues by providing objective information have failed just because they have always assumed full rationality in decision-making. In any case, with the advent of big data, more than ever, official statistics should take into account knowledge on bounded rationality in order to fulfill it's mission of improving decision-making for pursuing sustainable development goals.
That's why study of bounded rationality and the heuristics used in decision-making should be a central concern for official statistics, as well as for monitoring and evaluation research.
Is data visualization linked to heuristics? Does a good visualization present information in a way that it fits an heuristic model and reduces the cost of processing? In this case, a good understanding of the heuristics used by decision-makers can help improve data visualization.