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Figure – Poker final hand rankings. Poker is a typical example of bounded rationality in our daily lives. Without having all the information available, you still have to make a decision. In one of his works, Herbert Simon states: “boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information“.

[…] Bounded rationality is the idea that in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. It was proposed by Herbert Simon as an alternative basis for the mathematical modelling of decision making, as used in economics and related disciplines; it complements rationality as optimization, which views decision making as a fully rational process of finding an optimal choice given the information available. Another way to look at bounded rationality is that, because decision-makers lack the ability and resources to arrive at the optimal solution, they instead apply their rationality only after having greatly simplified the choices available. Thus the decision-maker is a satisfier, one seeking a satisfactory solution rather than the optimal one. Simon used the analogy of a pair of scissors, where one blade is the “cognitive limitations” of actual humans and the other the “structures of the environment”; minds with limited cognitive resources can thus be successful by exploiting pre-existing structure and regularity in the environment. Some models of human behaviour in the social sciences assume that humans can be reasonably approximated or described as “rational” entities (see for example rational choice theory). Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. The concept of bounded rationality revises this assumption to account for the fact that perfectly rational decisions are often not feasible in practice due to the finite computational resources available for making them. […] In Wikipedia, (link).

Book cover – Herbert A. Simon. Models of Bounded Rationality, Volume 1, Economic Analysis and Public Policy, MIT Press 1984. The Nobel Prize in Economics was awarded to Herbert Simon in 1978. At Carnegie-Mellon University he holds the title of Professor of Computer Science and Psychology. These two facts together delineate the range and uniqueness of his contributions in creating meaningful interactions among fields that developed in isolation but that are all concerned with human decision-making and problem-solving processes. In particular, Simon has brought the insights of decision theory, organization theory (especially as it applies to the business firm), behavior modeling, cognitive psychology, and the study of artificial intelligence to bear on economic questions. This has led not only to new conceptual dimensions for theoretical constructions, but also to a new humanizing realism in economics, a way of taking into account and dealing with human behavior and interactions that lie at the root of all economic activity. The sixty papers and essays contained in these two volumes are grouped under eight sections, each with a brief introductory essay. These are: Some Questions of Public Policy, Dynamic Programming Under Uncertainty; Technological Change; The Structure of Economic Systems; The Business Firm as an Organization; The Economics of Information Processing; Economics and Psychology; and Substantive and Procedural Reality. Most of Simon’s papers on classical and neoclassical economic theory are contained in volume one. The second volume collects his papers on behavioral theory, with some overlap between the two volumes. (from MIT).

Animated Video – Lively RSA Animate [April 2010], adapted from Dan Pink‘s talk at the RSA (below), illustrates the hidden truths behind what really motivates us at home and in the workplace. [Inspired from the work of Economics professor Dan Ariely at MIT along with his colleagues].

What drives us? Some quotes: […] Once the task called for even rudimentary COGNITIVE skills a larger reward led to poorer performance […] Once you get above rudimentary cognitive skills, rewards do not work that way [linear], this defies the laws of behavioural physics ! […] But when a task gets more complicated, it requires some conceptual, creative thinking, these kind of motivators do not work any more […] Higher incentives led to worse performance. […] Fact: Money is a motivator. In a strange way. If you don’t pay enough, people won’t be motivated. But now there is another paradox. The best use of money, and that is: pay people enough to take the issue of money off the table. […] …Socialism…??

[…] Most upper-management and sales force personnel, as well as workers in many other jobs, are paid based on performance, which is widely perceived as motivating effort and enhancing productivity relative to non-contingent pay schemes. However, psychological research suggests that excessive rewards can in some cases produce supra-optimal motivation, resulting in a decline in performance. To test whether very high monetary rewards can decrease performance, we conducted a set of experiments at MIT, the University of Chicago, and rural India. Subjects in our experiment worked on different tasks and received performance-contingent payments that varied in amount from small to large relative to their typical levels of pay. With some important exceptions, we observed that high reward levels can have detrimental effects on performance. […] abstract, Dan Ariely, Uri Gneezy, George Loewenstein, and Nina Mazar, “Large Stakes and Big Mistakes“, Federal Reserve Bank of Boston Working paper no. 05-11, Research Center for Behavioral Economics and Decision-Making, US, July 2005. [PDF available here] (improved 2009 version below)

Video lecture – On the surprising science of motivation: analyst Daniel Pink examines the puzzle of motivation [Jul. 2009], starting with a fact that social scientists know but most managers don’t: Traditional rewards aren’t always as effective as we think. So maybe, there is a different way forward. [Inspired from the work of Economics professor Dan Ariely at MIT along with his colleagues].

[…] Payment-based performance is commonplace across many jobs in the marketplace. Many, if not most upper-management, sales force personnel, and workers in a wide variety of other jobs are rewarded for their effort based on observed measures of performance. The intuitive logic for performance-based compensation is to motivate individuals to increase their effort, and hence their output, and indeed there is some evidence that payment for performance can increase performance (Lazear, 2000). The expectation that increasing performance-contingent incentives will improve performance rests on two subsidiary assumptions: (1) that increasing performance-contingent incentives will lead to greater motivation and effort and (2) that this increase in motivation and effort will result in improved performance. The first assumption that transitory performance-based increases in pay will produce increased motivation and effort is generally accepted, although there are some notable exceptions. Gneezy and Rustichini (2000a), for example, have documented situations, both in laboratory and field experiments, in which people who were not paid at all exerted greater effort than those who were paid a small amount (see also Gneezy and Rustichini, 2000b; Frey and Jegen, 2001; Heyman and Ariely, 2004). These results show that in some situations paying a small amount in comparison to paying nothing seems to change the perceived nature of the task, which, if the amount of pay is not substantial, may result in a decline of motivation and effort.

Another situation in which effort may not respond in the expected fashion to a change in transitory wages is when workers have an earnings target that they apply narrowly. For example, Camerer, Babcock, Loewenstein and Thaler (1997) found that New York City cab drivers quit early on days when their hourly earnings were high and worked longer hours when their earnings were low. The authors speculated that the cab drivers may have had a daily earnings target beyond which their motivation to continue working dropped off. Although there appear to be exceptions to the generality of the positive relationship between pay and effort, our focus in this paper is on the second assumption – that an increase in motivation and effort will result in improved performance. The experiments we report address the question of whether increased effort necessarily leads to improved performance. Providing subjects with different levels of incentives, including incentives that were very high relative to their normal income, we examine whether, across a variety of different tasks, an increase in contingent pay leads to an improvement or decline in performance. We find that in some cases, and in fact most of the cases we examined, very high incentives result in a decrease in performance. These results provide a counterexample to the assumption that an increase in motivation and effort will always result in improved performance. […] in Dan Ariely, Uri Gneezy, George Loewenstein, and Nina Mazar, “Large Stakes and Big Mistakes“, Review of Economic Studies (2009) 75, 1-19 0034-6527/09. [PDF available here]

Now, these are not stories, these are facts. These are one of the most robust findings in social science,… yet, one of the most ignored [sic]. And they keep coming in. Such as the fallacy of the supply and demand model (March 2008). Anyway, enough good material (a simple paper with profound implications)… for one day. But hey, …Oh, if you are still wondering what other paper inspired the specific drawings at minute 7′:40” and on, in the first video over this post, well, here it is: Kristina Shampan’er and Dan Ariely (2007), “How Small is Zero Price? The True Value of Free Products“, in Marketing Science. Vol. 26, No. 6, 742 – 757. [PDF available here]… Got it ?!

 

Dynamic Optimization Problems (DOP) solved by Swarm Intelligence (dynamic environment) - Vitorino Ramos

a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)

Swarm adaptive response over time, under sever dynamics

b) Swarm adaptive response over time, under severe dynamics, over the dynamic environment on the left (a).

Figs. – Check animated pictures in here. (a) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (b) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (left), describing a Dynamic Optimization (DO) Control Problem. Over each foraging step, the swarm self-regulates his population and keeps tracking the extrema (44 frames in total).

 [] Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, in Artificial Life X – Proc. of the Tenth Int. Conf. on the Simulation and Synthesis of Living Systems, L.M. Rocha, L.S. Yaeger, M.A. Bedau, D. Floreano, R.L. Goldstone and A. Vespignani (Eds.), MIT Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June 3-7, 2006.

PDF paper.

Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. Termite colonies – for instance – build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS’s were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different sub-populations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work. 

 

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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