Suboptimal Behavior Research Articles

The Benefits of Forced Experimentation: Striking Evidence from the London Underground Network*

AbstractWe present evidence that a significant fraction of commuters on the London Underground do not travel on their optimal route. We show that a strike on the Underground, which forced many commuters to experiment with new routes, brought lasting changes in behavior. This effect is stronger for commuters who live in areas where the Underground map is more distorted, which points to the importance of informational imperfections. Information resulting from the strike improved network efficiency. Search costs alone are unlikely to explain the suboptimal behavior.

Suboptimal behavior in strategy-proof mechanisms: Evidence from the residency match

Strategy-proof mechanisms eliminate the possibility for gain from strategic misrepresentation of preferences. If market participants respond optimally, these mechanisms permit the observation of true preferences and avoid the implicit punishment of market participants who do not try to “game the system.” Using new data from a flagship application of the matching literature—the medical residency match—I study if these potential benefits are fully realized. I present evidence that some students pursue futile attempts at strategic misrepresentation, and I examine the causes and correlates of this behavior. These results inform the assessment of the costs and benefits of strategy-proof mechanisms and demonstrate broad challenges in mechanism design.

Design and Implementation of a Low Complexity Multiuser Detector for Hybrid CDMA Systems

In hybrid CDMA systems, multiuser detection (MUD) algorithms are adopted at the base station to reduce both multiple access and inter-symbol interference by exploitingspace-time (ST) signal processing techniques. Linear ST-MUD algorithms solve a linear problem where the system matrix has a block-Toeplitz shape. While exact inversion techniques impose an intolerable computational load, reduced complexity algorithms may be efficiently employed even if they show suboptimal behavior introducing performance degradation and nearfar effects. The block-Fourier MUD algorithm is generally considered the most effective one. However, the block-Bareiss MUD algorithm, that has been recently reintroduced, shows also good performance and low computational complexity comparingfavorably with the block-Fourier one. In this paper, both MUD algorithms will be compared, along with other well known ones, in terms of complexity, performance figures, hardware feasibility and implementation issues. Finally a short hardware description of the block-Bareiss and block-Fourier algorithms will be presented along with the FPGA (Field Programmable Gate Array) implementation of the block-Fourier using standard VHDL (VHSIC Hardware Description Language) design.

Subjective Well-Being and Public Policy

Measures of subjective well-being have gained substantial attention in economics as quantitative approximations of individual welfare. They allow researchers to study relevant determinants of welfare on an individual as well as on a societal level. These determinants might not to be easily detectable in observable behavior. By referring to the recent well-being literature, we provide a selection of determinants of well-being that are important for public policy and show how the analysis of subjective well-being is applied as a complementary analytical tool for policy evaluation. We highlight the use of these measures for guiding public policy in areas that might involve suboptimal behavior. We also discuss some challenges for future research that are associated with the choice of evaluation metrics, the role of aspiration and adaption in evaluations, and utility misprediction.

The Cuckoo’s Nest Approach for Co-Creating Business Ecosystems in Smart Cities

The development of business ecosystems in smart cities is currently hampered by the absence of established approaches for facilitating long-term value and sustainability. In our view, the underlying reason is the lack of collective action involving various organizations in the design process. Collective action for the good of the whole ecosystem does not take place in existing participatory practices because of the dominating role of a single customer or designer organization (in urban development projects typically the owner-developer or lead architect), who uses their bargaining and decision-making power over others. This leads to sub-optimal behaviour where the system is optimized for the goals of one strong organization instead of collectively developed system-level goals of the business ecosystem as a whole. The Cuckoo’s Nest approach addresses this problem by inviting various expert organizations to design the system and assigning each organization design rights for the ecosystem and its system-level goal. The Cuckoo’s Nest approach enhances collective action among the organizations by making individuals from various organizations consider the interests, goals, objectives, and value-adding elements of other organizations – not just those of their own organizations. With the Cuckoo’s Nest approach, the business ecosystem comes first, and single organizations’ goals or specific design features come second. This article discusses the outcomes of two workshops where the Cuckoo’s Nest approach was used for the purpose of developing business ecosystems in connection with smart city development projects within the Helsinki Metropolitan Area. We outline the steps involved in the Cuckoo’s Nest approach and how they were applied in these two smart city projects, and we describe how it is being refined for further use in other locations and contexts. (Less)

General Equilibrium Theory with Imperfect Information, Bounded Rationality, Incomplete Markets, and the Wisdom of Crowds

I study general equilibrium theory with incomplete information. When agents are not fully informed, they can end up purchasing a bundle of goods that is far from optimal. General equilibrium theory falls short of providing a satisfactory explanation of the ability of real markets to deliver good outcomes under these circumstances. I introduce the wisdom of crowds as a corrective for suboptimal individual behaviour. The wisdom of crowds refers to the empirically observed ability of crowds to show collective intelligence even when their constituent individuals do not. I show that when crowds are wise, aggregate demand, aggregate production and prices all approach their ex-post Pareto efficient levels. In a neighbourhood of equilibrium, prices follow a martingale process, providing a general equilibrium derivation of the efficient market hypothesis. A spot market that opens after the resolution of uncertainty delivers an outcome that is ex-post Pareto efficient. This is achieved without any contingent commodities or securities, and agents who act `naively' and needn't have any ability to predict future prices.

Recency, Records, and Recaps

Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse selection (or “lemons”) problem and find that learning models that heavily discount past information (i.e., display recency bias) explain patterns of behavior better than Nash, cursed, or behavioral equilibrium. Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages (“recaps”) gets individuals most of the way to optimality. Thus, recency effects are not solely due to limited memory but stem from some other form of cognitive constraints. Our results show the importance of going beyond static optimization and incorporating features of human learning into economic models used in both understanding phenomena and designing market institutions.

Co-Evolution of Social Learning and Evolutionary Preparedness in Dangerous Environments

Danger is a fundamental aspect of the lives of most animals. Adaptive behavior therefore requires avoiding actions, objects, and environments associated with danger. Previous research has shown that humans and non-human animals can avoid such dangers through two types of behavioral adaptions, (i) genetic preparedness to avoid certain stimuli or actions, and (ii) social learning. These adaptive mechanisms reduce the fitness costs associated with danger but still allow flexible behavior. Despite the empirical prevalence and importance of both these mechanisms, it is unclear when they evolve and how they interact. We used evolutionary agent-based simulations, incorporating empirically based learning mechanisms, to clarify if preparedness and social learning typically both evolve in dangerous environments, and if these mechanisms generally interact synergistically or antagonistically. Our simulations showed that preparedness and social learning often co-evolve because they provide complimentary benefits: genetic preparedness reduced foraging efficiency, but resulted in a higher rate of survival in dangerous environments, while social learning generally came to dominate the population, especially when the environment was stochastic. However, even in this case, genetic preparedness reliably evolved. Broadly, our results indicate that the relationship between preparedness and social learning is important as it can result in trade-offs between behavioral flexibility and safety, which can lead to seemingly suboptimal behavior if the evolutionary environment of the organism is not taken into account.

Suboptimal Choice in Pigeons: Stimulus Value Predicts Choice over Frequencies.

Pigeons have shown suboptimal gambling-like behavior when preferring a stimulus that infrequently signals reliable reinforcement over alternatives that provide greater reinforcement overall. As a mechanism for this behavior, recent research proposed that the stimulus value of alternatives with more reliable signals for reinforcement will be preferred relatively independently of their frequencies. The present study tested this hypothesis using a simplified design of a Discriminative alternative that, 50% of the time, led to either a signal for 100% reinforcement or a blackout period indicative of 0% reinforcement against a Nondiscriminative alternative that always led to a signal that predicted 50% reinforcement. Pigeons showed a strong preference for the Discriminative alternative that remained despite reducing the frequency of the signal for reinforcement in subsequent phases to 25% and then 12.5%. In Experiment 2, using the original design of Experiment 1, the stimulus following choice of the Nondiscriminative alternative was increased to 75% and then to 100%. Results showed that preference for the Discriminative alternative decreased only when the signals for reinforcement for the two alternatives predicted the same probability of reinforcement. The ability of several models to predict this behavior are discussed, but the terminal link stimulus value offers the most parsimonious account of this suboptimal behavior.

The Effect of Risk Aversion and Loss Aversion on Equity-Linked Life Insurance With Surrender Guarantees

This paper values equity-linked life insurance contracts with surrender guarantees for risk averse and loss averse policyholders in continuous time. With increasing risk aversion, policyholders surrender their insurances for higher values of the underlying equity fund, compared to an optimally stopped insurance contract, leading to substantial losses. Moreover, high discounting amplifies suboptimal surrender behavior. Loss averse policyholders display a different surrender behavior: Such policyholders surrender only policies for which the surrender benefit represents a large gain, while holding on to less successful contracts, so that the disposition effect increases the contract value relative to a contract stopped by a risk averse policyholder.

Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.

A bio-inspired evolutionary algorithm: allostatic optimisation

Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which explains how the configuration of the internal state IS in different organs allows reaching stability when an unbalance condition is presented. In this paper, a novel biologically-inspired algorithm called allostatic optimisation AO is proposed for solving optimisation problems. In AO, individuals emulate the IS of different organs. In the approach, each individual is improved by using numerical operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimisation algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

Valuing American Options Using Fast Recursive Projections

We introduce a fast and widely applicable numerical pricing method that uses recursive projections. We characterize its convergence speed. We find that the early exercise boundary of an American call option on a discrete dividend paying stock is higher under the Merton and Heston models than under the Black-Scholes model, as opposed to the continuous dividend case. A large database of call options on stocks with quarterly dividends shows that adding stochastic volatility and jumps to the Black-Scholes benchmark reduces the amount foregone by call holders failing to optimally exercise by 25%. Transaction fees cannot fully explain the suboptimal behavior.

Myopic Law Preferences and Non-coercive Market Equilibria

This paper investigates the economic conditions under which the performance of a Judiciary does not impede noncoercive fair socioeconomic allocations under “Strotz-myopia” regarding the law variable, i.e., under a static view of it in an otherwise dynamic context. The law, here, is the positive factor by which consumption volume is multiplied as a result of law introduction in an otherwise fully private social economy. Lexicographic preferences regarding the law is the keyword in establishing non-coercive equilibria either in the static context of a stone-age economy or in the dynamic context of a jungle economy, given in the latter the presence of farsightedness. Nevertheless, such equilibria are found here to exist even under myopia and regardless the presence of lexicographic preferences. We first detect them within a fully private social economy, and we next qualify them by introducing the Judiciary as state officials. The optimality regarding state finances imposes additional restrictions in establishing myopic non-coercive equilibria. In any case, an equilibrium will be stable if it is not influenced by the homotheticity or not of the preferences, i.e., by income distribution considerations. So, any suboptimal behaviour of the Judiciary should be attributed exclusively to the suboptimality of state finances: Macroeconomics does affect law administration.

Law, Coercion and Socioeconomic Equilibrium

Abstract This paper investigates the economic conditions under which the performance of a Judiciary does not impede non-coercive fair socioeconomic allocations under “Strotz-myopia” regarding the law variable, i.e. under a static view of it in an otherwise dynamic context. The law, here, is the positive factor by which consumption volume is multiplied as a result of law introduction in an otherwise fully private social economy. Lexicographic preferences regarding the law is the keyword in establishing non-coercive equilibria either in the static context of a stone-age economy or in the dynamic context of a jungle economy, given in the latter the presence of farsightedness. Nevertheless, such equilibria are found here to exist even under myopia and regardless the presence of lexicographic preferences. We first detect them within a fully private social economy, and we next qualify them by introducing the Judiciary as state officials. The optimality regarding state finances imposes additional restrictions in establishing myopic noncoercive equilibria. In any case, an equilibrium will be stable if it is not influenced by the homotheticity or not of the preferences, i.e. by income distribution considerations. So, any suboptimal behaviour of the Judiciary should be attributed exclusively to the suboptimality of state finances: Macroeconomics does affect law administration.

Nudging investors big and small toward better decisions.

Investors significantly reduce their future returns by selecting mutual funds with higher fees, allured by higher past returns that do not predict future performance. This suboptimal behavior, which can roughly halve an investor’s retirement savings, is driven by 2 psychological factors. One factor is difficulty comprehending rate information, which is critical given that mutual fund fees and returns are typically communicated in percentages. A second factor is devaluing small differences in returns or fees (i.e., a peanuts effect). These 2 factors interact such that large investors benefit when fees are stated in currency (as opposed to percentages), whereas small investors benefit from returns stated in currency. These striking results suggest behavioral interventions that are tailored specifically for small and large investors.

Weighted-noise threshold based channel estimation for OFDM systems

Orthogonal frequency division multiplexing (OFDM) technology is the key to evolving telecommunication standards including 3GPP-LTE Advanced and WiMAX. Reliability of any OFDM system increases with improved mean square error performance (MSE) of its channel estimator (CE). Particularly, a least squares (LS) based CE incorporating a time-domain denoising threshold, enables better MSE performance, while avoiding the need for a-priori knowledge of channel statistics (KCS). Existing optimal time-domain thresholds exhibit suboptimal behavior for completely unavailable KCS environments. This is because they involve consistent estimation of one or more KCS parameters, and corresponding estimation errors introduce severe degradation in MSE performance of the CE. To overcome the MSE degradation, this paper proposes a weighted-noise threshold, by introducing a modified hypothesis-testing-problem (HTP) interpretation. Derivation of resulting analytical MSE expression is also provided. Results of OFDM system simulations carried out in rayleigh faded ITU-TU6 and WiMAX-SUI4 channel environments with U-shaped power spectral densities, are presented. The performance results show that, compared to many of the existing thresholds, the proposed threshold renders better MSE performance to the CE and higher reliability to the OFDM system in terms of better bit error rate (BER) performance.

Suboptimal Behavior in Strategy-Proof Mechanisms: Evidence from the Residency Match

Strategy-proof mechanisms eliminate the possibility for gain from strategic misrepresentation of preferences. If market participants respond optimally, these mechanisms permit the observation of true preferences and avoid the implicit punishment of market participants who do not try to "game the system." Using new data from a flagship application of the matching literature - the medical residency match - I study if these potential benefits are fully realized. I present evidence that some students pursue futile attempts at strategic misrepresentation, and examine the causes and correlates of this behavior. These results inform the assessment of the costs and benefits of strategy-proof mechanisms, and demonstrate broad challenges in mechanism design.

Evolving to Generalize: Trading Precision for Speed

Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning.

Bayesian ideal observer predicts weak forms of blindsight in normal observers

It has been reported that damage to V1 can lead to blindsight: such patients can discriminate simple visual stimuli above chance and yet allegedly report no conscious visual percept. It has also been suggested that similar forms of unconscious vision can be demonstrated in normal observers, i.e. under certain experimental conditions (e.g. low-contrast masked stimuli) subjects can perform above-chance discrimination even when rating confidence/stimulus visibility at the lowest level. However, from a signal detection theoretic (SDT) perspective, these findings may be trivial: subjects may report low confidence/invisibility only because the signal falls below an arbitrary confidence/visibility criterion, rather than being truly invisible. We therefore investigated whether perceptual discrimination without subjective awareness can be demonstrated unequivocally by the rigorous standards of ideal observer analyses. To bypass the criterion problem mentioned above, we probed confidence with a 2-interval forced-choice procedure (Barthelme & Mamassian, 2009, PLoS CB): normal observers viewed paired intervals of masked stimuli, discriminated the orientation of each (left/right tilted), and then chose which decision they were more confident in (Expt 1) or which stimulus was more visible (Expt 2). Importantly, in one interval the stimulus had zero contrast, meaning above-chance orientation discrimination was impossible. Strong evidence for blindsight would consist of subjects' discriminating the stimulus above-chance yet failing to consistently select it as more confident/visible relative to the blank interval. Such suboptimal behavior would violate common models of perception. In both experiments, intriguingly, we found that observers judged confidence/visibility with lower sensitivity than they could discriminate orientation, according to SDT measures, suggesting there may be evidence for compromised subjective awareness. Crucially, however, a Bayesian ideal observer model predicts similar behavior. We argue that traditional SDT analyses make unrealistic assumptions about what observers can do in these tasks, and show with simulations how misleading results could be obtained via conventional procedures. Meeting abstract presented at VSS 2015. Language: en