The effectiveness of yoga for the improvement of well-being and resilience to stress in the workplace

Summary by Keith Robinson Jr.

Summary:
Pressure to achieve outcomes and perform well in the workplace can ultimately diminish workplace efficiency, placing employee physical health and psychological well-being at risk. Extant literature has illustrated strong links between stress and related conditions such as anxiety, depression, heart disease, back pain, headaches, and gastrointestinal disturbances. In 2008, National Institute for Health and Clinical Excellence estimated 8.2 billion pounds in losses per year due to a lack of physical activity among workers. Studies have shown that participation in yoga has both physical and psychological benefits; reduces stress, increases cardiopulmonary and central nervous system function, reduces blood pressure, fatigue, and symptoms of  anxiety and depression.

The purpose of the study was to examine the effectiveness of yoga in improving emotional well-being and increasing resilience to stress among employees. This study focused on the degree of stress and emotional well-being among university staff. The sample consisted of 48 of a British university's employees self-selected through the school's intranet and flyer postings. Participants were split into two randomized groups, a control group of 24 members and a yoga intervention group of 24 members that were asked to engage in at least one of three 60-minute lunchtime classes per week for six weeks. Additionally, the participants received a yoga CD that included a guided 35-minute home practice session and a form to record. Variables were measured at their baseline and at the conclusion of the study.

On average, participants in the yoga group attended 1.15 classes per week. Any participants that took less than 6 classes over the course of the study were excluded from analysis. 9 participants (45%) returned their home practice forms. Yoga group participants improved 2-5 times more than those in the control group throughout the study in seven out of eight domains. The yoga group illustrated marked improvements in feelings of clear-mindedness, composure, elation, energy, and confidence. Additionally, the group reported increased life purpose and satisfaction and feelings of greater self-confidence during high stress situations. The results indicated that even a six-week program of yoga had positive effects on emotional well-being and resilience to stress among employees at a British university.

Critique:
It would be interesting to see the effect of the implementation of this study across different professions, specifically those seen as contributing generally higher stress situations than others. The authors understood the limitations of their study which could be examined and analyzed to improve future studies. The authors acknowledge that repeated trials utilizing the same assessment tools, varying yoga styles, and across different populations are necessary before the effects of yoga can be generalized to the wider population.


Source: https://www.researchgate.net/publication/43024160_The_effectiveness_of_yoga_for_the_improvement_of_well-being_and_resilience_to_stress_in_the_workplace

Effectiveness of a worksite lifestyle intervention on vitality, work engagement, productivity, and sick leave: results of a randomized controlled trial

Summary and critique by: Ian Abplanalp

Summary:

In a study conducted in 2013 a group of Scandinavian scientist held a study that studied worksite health style intervention techniques as a means to prolong workforce participation. This study measured four components vitality, work engagement, productivity, and sick leave. 

The study invited all individuals from two different academic hospitals as long as they were above 45 years of age, and worked more than 16 hours a week. The participants were randomized into two groups a control and an intervention group, both groups received pre-trial health measurements which were controlled for. 

Both groups were presented with literature about healthy lifestyle, while the intervention group was introduced to 6 month vitality practice. Vitality practice can in the form of meetings that occurred three times a week, and were led with by a wellness coach. The wellness meetings were broken into three different types, one consisted of a yoga session, one aerobic exercise class, and a meeting with a wellness coach. Each meeting type took place once a week, and participants were asked to perform 45 minutes of exercise of the same caliber of the fitness meetings outside scheduled meetings per week. The participants of the  intervention group were provided with free fruit at the end of workout session. Research design and drop out information can be seen in the figure below.  

The intervention group showed no adverse side effects to being subjected to the exercise regiment. However, despite their effort there was no statistical significance to the individuals in the control group across vitality, work engagement, work leave and productivity. Intervention participants did show a positive trend improving their vitality scores by 1.9 compared to the .10 of the control group, but this was not statistically significant. Both the aerobic and yoga subgroups showed this positive trend of improving workplace vitality, when used in high compliance (sticking to the assigned workout program). Yoga showed a stronger positive trend to improving workplace vitality, than aerobic workouts, yoga showed its strongest correlation when used in conjunction with aerobic exercise. 

Critique:

While the article discusses the lack of statistical significance of the intervention I would argue that a qualitative study follow up with the individuals would add more nuance to research. It would allow for a deeper understanding of what intangibles led to a positive trend in the intervention groups vitality, to better construct future research. The time span of the study was also very short for looking at the given problem of extending the workforce participation, only going on for a year. As well as the study was only conducted on older individuals, it raises the question of if implemented over the course of 20 years since being a young employee how would that effect the variables instead of just a one year time frame of older adults. However the article did a good job, albeit unsuccessfully, of beginning to answer a complicated question of how extend the lifespan of the workforce. 

Impact of Adoption of Yoga Way of Life on the Emotional Intelligence of Managers

Hasmukh Adhia, H.R. Nagendra, and B. Mahadevan

by Oddinigwe Onyemenem

Summary

This paper builds on the thread of previous studies about utilizing the concept of EI defined by earlier researchers to measure managerial performance, and explores the yoga way of life as a potential tool to influence the EI of individuals. This paper studies the impact of the yoga way of life on emotional intelligence (EI) by using data collected from 60 managers in a business enterprise and reports enhanced EI because of the practice of yoga. The popular perception that a high intelligence quotient (IQ) is not necessarily a good predictor of professional and personal success has led to a growing interest in understanding the role of EI in improving the performance of business managers. The paper hypothesizes that practicing the yoga way of life may bring about a complete transformation of one’s personality, on the physical, mental, emotional, and spiritual levels.

The paper defines EI as the ability to perceive emotions, to access and generate emotions to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions to promote emotional and intellectual growth. In a referenced study which was conducted in 200 large, global companies, it revealed that at the highest levels of a company, EI is essential for leadership. A person can have first class training, an incisive mind, and a large supply of good ideas, but without EI it is unlikely that he or she will make a great leader.

This study was conducted in a unit with about 120 people in the managerial cadre and more than 1000 in the workers’ category. Most of the employees reside in the township of the company, which made it easy to conduct the intervention of yoga.

Below is a summary of the study’s methodology:

·       The participants were divided into two equal groups of 42. Group 1 was the yoga group and group 2 was the physical exercise group (control group).

·       The yoga group was given 30 hours of yoga practice (75 minutes every day) and 25 hours of theory lectures on the philosophy of yoga spread over six weeks.

·       The control group was also given training in normal physical workout for an equal number of hours, and lectures on the success factors in life based on modern thought (that seeks to achieve success by systematic control of factors within one’s area of influence).

·       To test the hypothesis, EI was measured for both the groups, before and after the study, with the help of a standard self-reported questionnaire. In addition, measurements of certain physical parameters such as weight, body mass index, blood pressure, and blood sugar were taken for all, before and after the study.

Results from the study showed an increase in EI for the yoga group when comparing results that were taken before and after the study. The paper highlights certain aspects that need to be followed in implementing yoga as a way of life in organizations. The first step is to convince top managers to buy-in on the benefits of implementing yoga as a way of life. One of the potential challenges to the yoga way of life is the apprehension of renunciation effects in a productive working environment characteristic of business organizations, which look to nurture the killer instinct of their executives which is attributed to a lack of understanding of the true concepts of yoga. Next step, which the article considers the tougher part, is finding the right people to train the company executives on a continuous basis and should be periodically repeated.

The study suggests that the yoga way of life could potentially contribute to improving performance of managers, and improving their satisfaction levels. At a philosophical level, the yoga way of life seeks to unite the individual consciousness with universal consciousness. At the empirical level, the efficacy of scientific scrutiny needs to be tested by conducting more studies.

Critique

The study provided some useful insights and approached the issue from various angles. It rightly suggested further studies in this area like the sleep study. It appears that organizations are generally not paying attention or doing enough to promote activities such as yoga and sleep which can greatly improve the quality of lives for both individual contributors and management. It would be interesting to see this study replicated, but getting participants from various organizations rather than just one.

Source

https://www.sciencedirect.com/science/article/pii/S0970389610000042

Summary of Findings:  Sleep (5 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in October 2017 regarding Sleep as an Analytic Modifier specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Sleep is a naturally recurring state of mind and body, characterized by altered consciousness, relatively inhibited sensory activity, inhibition of nearly all voluntary muscles, and reduced interactions with surroundings. It is distinguished from wakefulness by a decreased ability to react to stimuli, but is more easily reversed than the state of being comatose. Sleep influences memory, emotional stability, and physical recovery. In terms of intelligence analysis, sleep is a technique modifier, not an analytic method.

Strengths:

• Allows the body to repair muscles, ligaments, and tissues
• Better cognitive performance
• Strengthens the immune system
• Keeps you alert, able to focus, and less irritable
• If you can convince your team to make the change it is a small, low cost, easy step to increase productivity
• Decreases risk for health conditions

Weaknesses:

• It’s a complex study that contains many confounding variables
• It is very subjective as sleep requirement of individual differs depending on their health, age and physical activity
• The proper amount of sleep is different for each individual, along with a host of factors that can affect sleep, such as exercise and diet
• Oversleeping can affect energy levels
• Despite research, it’s hard to get people to buy into changing their habits
• Long deep sleep might lead to muscle soreness
• Overtime, sleep deprivation can lead to diabetes and obesity5

How-To:

1. Avoid sugar, caffeine, and blue light in the hours before bed
2. Lay down in a comfortable position
3. Close eyes. Breathe deeply

Application of Technique:

For the application of sleep as a modifier to express how important sleep is to cognitive function, the class participated in three exercises. The class competed in these exercises against two baseline sleep deprived individuals who stayed up through the previous night (thank you Matt and Sam). The class first took a baseline sleep deprivation quiz to get a quantified measurement on how sleep deprived they were walking into the assessment. The class then participated in a short-term memory game, to determine how memory was impacted. The class’s final exercise was to participate in the cognitive reflection test in order to test the cognitive power of the class.

The scores of each class member were measured in regards to how many they had right, and compared against the individuals who stayed up through the previous night and how different they were. Upon discussion of the class, there were vastly different results from our two sleep deprived individuals. Matt, who performed well on the memory test, stated that even though he did well, he was not as confident as he would’ve been had he been properly rested.   

Links for the exercises performed by the class below. For participation at home, scores of sleep deprived individuals were as follows: Sam (Baseline Moderate/High, Memory 2/20, Cognitive Reflection 1/3) and Matt (Baseline Moderate/High, Memory 15/20, and Cognitive Reflection 2/3).

For Further Information:

1. Sleep Deprivation Baseline
2. Memory Test
3. Cognitive Reflection Test

Effects of Sleep Deprivation on Performance: A Meta-Analysis

Matthew Haines

Summary:

                This meta-analysis attempts to make the distinction that sleep deprivation has a negative effect on performance. The authors focused the scope of the meta-analysis to include cognitive performance, motor performance, and mood. The meta-analysis included studies conducted from 1984-1992 and the study had to examine the effects of either partial sleep deprivation, long-term sleep deprivation, and short-term sleep deprivation. The studies were also categorized based on complexity and duration of the task, whether it be cognitive or negative. The study concluded that sleep deprivation caused a performance level decrease 1.37 standard deviations from the mean. This study also showed that the mean performance of sleep deprived subjects was in the 9^th percentile of subjects that were not sleep deprived.  These findings aid the validity of studies that show sleep deprivation has a negative effect on performance. This finding contradicts most of the findings being put out by introductory psychology books.

Critique:

                This study is an extensive analysis of sleep studies conducted from 1984-1992 and it does a good job of highlighting the different aspects of sleep deprivation. The authors try to address all confounding variables to increase the meta-analysis’s validity and they do a good job of collecting a decent sample size. However, I would like to see a more in-depth analysis into the data. It would help increase the validity of the study even more to explain how the data was normalized, if they accounted for variability, or if the conducted a study that delved into the variables more explicitly. 

https://watermark.silverchair.com/sleep-19-4-318.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAfUwggHxBgkqhkiG9w0BBwagggHiMIIB3gIBADCCAdcGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMSiRuKuQxQ1JiDcYtAgEQgIIBqClI0H_1aoKooCIA-78IBxJb9JsvkgeDp05a4Ur6vqTU1hy72RbpFhsCeVfrAu0CpO90JinWXPNYPUxRvkHUssFwT0dGNm1D2o18cjhgmZteflWJ9juRfmVlh89tPO17WEaMjCu0nCY_ZhU3nxn9qeEuerkTUHe3m0L0Ot_gm5FapOghGvKdkkXVpmZuviE5MavNx698xtQTVCFd_e3VrNZ2SfvHO5LJa_3Smv7XjAr-SWO1F3U4sL9IXfc1ROLaERtlqsH02_Dn5hMPW74aRlLZUPQY3uiwkGacIOkK6i5HoH6MwgRWikeIaXqIOOXuq5l_34OmvNlW6uPVYDwKbXdLFzfjuLuTnkQP-awD6SznsPIKZeYlpdVh7mhCYl_isGhEM7U5tQmz6FbPtxHS2I1s23tsgrpX8ukZrl_0kMq2u4MK7vFCCURcuVP7EKC7WLFG04iVPdUfw3P5WSwge3zyKjhMY95e8w4ML-V6ALJntxdmry77n-bJKRJVtveh-6gsdWz5l4lANEz0x19dNrBLpDTQIkvMS0qDe3QecukM2q2kHlkd9Z8

Why Sleep Deprivation Makes You Stupid, Slow, and Dangerous

By: Heather Davis, EdD 

Summary: 

The article discusses the ill effect of Sleep deprivation as commonplace and its effects on cognitive function, health, and mood. It discusses the cost associated with it, the effect on employee performance and death related to it. This happens as lack of sleep leads to the slowing of reaction time, lack of alertness, attention, and vigilance, less is agreed-upon about the effects of sleep deprivation on higher level cognitive functions related to perception, memory, and executive functioning.

It discusses some of the important research the findings like

1. In Dawson and Reid's 1997 study, they found that 24-hour wakefulness produces the same performance on a hand-eye coordination task as when those same subjects had a blood alcohol concentration of 0.10%, the legal limit for intoxication in all 50 states.
2. Killgore, et al., 2007 found that sleep-deprived individuals demonstrate frustration intolerance, lack of empathy and moral judgment, impulse control, and inability to delay gratification.
3. Kahn-Greene et al., 2007 found that sleep deprivation also results in as much as a 25% increase in several dimensions of psychopathology such as clinical depression, anxiety, paranoia, mania, and borderline personality disorder.
4. Zhong, et al., 2005 demonstrated that lack of sleep causes increases in serum norepinephrine, the neurotransmitter responsible for the fight-or-flight stress response that results in increases in blood pressure and inflammation within blood vessel walls, commonly associated with heart attacks.
5. Knutson, Spiegel, Penev, & Cauter, 2007 found that lack of sleep also results in glucose intolerance, the body's ability to utilize consumed carbohydrates for energy instead of storing the calories as adipose tissue.

The article ends up bringing points for further discussion like

1. What strategies could we employ (or are you familiar with) to get teens and adults to get enough sleep, given our 24-hour society and prevalence of technology? 

2. As educators, administrators and parents and healthcare practitioners, what practices do we [unintentionally] employ that place productivity on a pedestal oversleep, good health, and a balanced lifestyle?

3. What evidence (even if anecdotal) do you have that sleep has impaired your own performance or health?

Critique: This article discusses different research and their findings with no information on any particular research. It discusses the psychological and physical issue with sleep deprivation, but no issue with effect on actual applicability. The study highlights the effects of sleep deprivation both in adults and kids. Despite, numerous studies on the effects of sleep, more research is needed in validating the effect of sleep deprivation on higher cognitive functions and whether or not it has temporary or permanent effects. Also, with so much emphasis on the health consequences of sleep deprivation, organizations still do not give it as much seriousness or attention as needed. 

Reference: https://www.altru.org/app/files/public/15566/205-Davis-Sleep.pdf

The Cumulative Cost of Additional Wakefulness: Dose-Response Effects on Neurobehavioral Functions and Sleep Physiology From Chronic Sleep Restriction and Total Sleep Deprivation

Researchers: Hans P.A. Van Dongen, Ph.D.; Greg Maislin, MS, MA; Janet M. Mullington, Ph.D.; David F. Dinges, PhD

By: Michael Pouch

Summary:

Through previous research it is well established that sleep cannot be completely eliminated without having neurobehavioral consequences, thus the purpose of this study added to the debate whether human sleep can be chronically reduced without consequences. Therefore, the researchers conducted a dose-response chronic sleep restriction experiment in which waking neurobehavioral and sleep physiological functions were monitored and compared to those for total sleep deprivation.
The researcher's design method for the sleep restriction experiment included 48 participants in a chronic sleep restriction experiment or in a total sleep deprivation experiment for 14 consecutive days. Both groups were monitored for physiologically and behaviorally changes under controlled conditions and with strict schedules for the time in bed. To get a reference point for the sleep restriction group, participants had one adaptation day and two baseline days with eight-hour sleep opportunities followed by randomization to one of three sleep doses four, six, or eight hours times in bed per night, which was maintained for 14 consecutive days. This was compared to the total sleep deprivation group involved three nights straight without sleep. Each study also involved 3 baselines (pre-deprivation) days and 3 recovery days.
Results of the sleep restriction group which saw sleep periods to 4 or 6 hours per night over 14 consecutive days saw significant cumulative, dose-dependent deficits in cognitive performance on all tasks. According to the sleepiness ratings, there were acute responses to sleep restriction but only small further increases on subsequent days and did not significantly differentiate the 4 to 6 hour of sleep conditions.  In addition, there was a magnitude of changes in performance over days of sleep restriction in the 6 hours of sleep period condition was between that observed for 4 to 8 hours of sleep period conditions. Researchers also saw similarities within cognitive performance deficits between the two groups were six hours or less sleep per night is equivalent to up to two sleep deprive nights
Researchers came to conclude that six hours or less sleep per night is a good reference point to say that this range of sleep produced cognitive performance deficits equivalent to up to 2 nights of total sleep deprivation. In addition, participants were largely unaware of these increasing cognitive deficits throughout the study. In the end, the results of the study shown that adding additional wakefulness over time has neurobiological cost.

Critique
Though it is well established that sleep cannot be completely eliminated without waking neurobehavioral consequences, the researchers added their own twist to this field by studying the effects of reducing the time for sleep during the work week or for even longer periods to see if there are any consequences from this sleep cycle. I found it interesting that even relatively moderate sleep restriction can seriously impair waking neurobehavioral functions in healthy adults. The researchers did not just measure sleep debt but took it a step further by placing restrictions on sleep and seeing how this impairs individuals. In my opinion, I feel the researchers made this more realistic by adding this feature to their study. Overall, I feel this study points out different sleep cycles and restrictions have consequences on an individual cognitive performance.

Reference:
Dongen, H. P., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The Cumulative Cost of Additional     Wakefulness: Dose-Response Effects on Neurobehavioral Functions and Sleep Physiology From     Chronic Sleep Restriction and Total Sleep Deprivation. Sleep, 26(2), 117-126.     doi:10.1093/sleep/26.2.117.
Reference found: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693.6032&rep=rep1&type=pdf

Sleep Deprivation: Impact on Cognitive Performance

Researchers Paula Alhola and Paivi Polo-Kantola examined the effects of sleep deprivation on cognitive thinking. First they differentiated between chronic partial and acute total sleep deprivation or SD. Then they compared the studies of chronic partial sleep deprivation versus acute total sleep deprivation.

On average, people need about 7.5 to 8 hours of sleep per night. However, the actual number varies by individual. The 2 sleep processes that a person goes through are called homeostatic process S and Circadian process C. Homeostatic processes regulate wakefulness while circadian processes regulate the onset and offset of sleep. Sleep cycles are regulated by the interaction of the 2 processes.

Sleep deprivation causes memory loss, high blood pressure, insulin resistance, impaired immune response, and adverse effects on mood. The researchers examined the full effect by using sleep deprivation as a research design. In the study, subjects were kept awake for 24-72 hours to examine acute total sleep deprivation, while the other subjects' sleep was restricted for several consecutive nights. Variables studied were short-term memory, long-term memory, visual, verbal, and auditory functions.  This study also accounted for the fact that some individuals need more sleep than others.

The researchers found that the effects of partial SD are not as well known as acute SD. Sleep deprivation studies need to have a larger sample as well. Results of their study found that aging does prolong wakefulness while impairing cognitive performance and that women handle prolonged wakefulness better than men mentally, but they recover slower physiologically. However, the effects on younger men and women is still unclear. Alhola and Kantola recommend a more thorough study be made.

Critique: Alhola and Kantola are correct in their recommendation that more thorough studies be made. With the emphasis on cognitive performance in the modern work force, it is essential that we understand the full impact of sleep deprivation on our brains. However, while they point out previous studies and criteria, they do not give a methodology of their own; merely stating that methodological issues be more thoroughly examined.

Citation: Alhola, Paula and Polo-Kantola, Paivi. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656292/.

THE RELATIONSHIP BETWEEN SLEEP AND WORK: A META-ANALYSIS

Summary and Critique by Claude Bingham

Summary

As part of his dissertation, Brett Litwiller conducted a meta-analysis of studies that examined sleep's effect on work quality. As this is a summary geared towards intelligence professionals, the findings will be limited to ideas that inform readers of this specific blog.

Litwiller starts by explaining that a recent study found 34% of American workers fell asleep unintentionally in the past 30 days. He defines sleep as a, "... state of immobility that consists of greatly diminished physical responsiveness and is more rapidly reversible than anesthesia or coma (Siegel, 2005)." Insufficient amounts of quality sleep, Litwiller says, leads directly to disease and workplace ineffectiveness. Sleep quality is obviously hard to quantify outside of hours of sleep completed.

Based on his literature review, he explains that supportive work environments actually reduce the effects of sleep deprivation but stressful environments will not only exacerbate their effects but also harm sleep quality in return. Analysis of source studies showed the following effects on effectiveness: increased accidents or mistakes, especially for monotonous tasks, increases in mental instability such as depression, mood changes, and burnout, and increases in work absences. Interestingly, sleep had no significant effect on how much people enjoyed their job, just how well they performed at it and connected with peers.

Critique

Meta-analyses are generally considered the most valid form of research as they have the benefit of testing already completed research results against each other. Litwiller was very keen to make sure results were compared only to like studies; only two studies examined actually used empirically validatable and testable definitions of sleep in their research. Likewise, most of the studies were based on self-reported results, something that can create incredibly drastic errors in data collection, considering the nature of sleep. These two factors make this meta-analysis slightly less credible than it would be otherwise, however, the sheer amount of data analysed showed that the results were mostly consistent, rarely contradictory, and showed a high correlation between more and better sleep, and better work production.

https://shareok.org/bitstream/handle/11244/10396/FinalDissertation.pdf?sequence=2&isAllowed=y

Decision-Making Under Conditions of Sleep Deprivation: Cognitive and Neural Consequences

Summary and Critique by: Jared Leets

Summary:

Scher, Zeithamova, and Williams begin by stating that the domains affected by sleep deprivation tended to involve complex integrating tasks. These tasks demand flexibility, innovation, and decision-making. There has been plenty of literature focusing on the role of decision-making in the prefrontal cortex. The article examined neural and cognitive changes in association with sleep deprivation. Subjects performed the decision-making tasks while undergoing fMRI scanning in the morning after getting 7-9 hours of sleep and then again at the same time on

Day 2 after remaining awake for 24 hours. Fifteen students participated in this study most of them from West Point and the remaining students attending the University of Texas. A PC-compatible laptop computer presented the visual images using a LCD projection system that was behind the MRI projecting a screen mounted in the bore 16 inches from the participant’s eyes. Participants viewed the images through a mirror placed on the MR head coil and responded to the task, via controllers given to them in their right hand.

Stimuli for the three decision tasks were novel abstract shapes. Every task asked participants to match perceptually similar shapes. In the two-alternative forced-choice task, participants viewed a shape on the top of the screen simultaneously with two test shapes on the bottom of the screen and were asked “Which one?” The participants’ task was to point to which

of the two shapes matched the exemplar shape. The subjects received information explaining that the shapes were hand-written scripts in a foreign language and would not match exactly. In the integrative decision, the task consisted of one exemplar shape and two other shapes with the question in the middle saying, “Exactly one?” The participants had to say either one or only one, of the test items were equal to the exemplar item. For every task there was a time limit, typically 3.6 seconds, and when the time limit passed the display was replaced with a blank screen until the new trial began.

The results indicated that a participant’s ability to make integrative decisions drastically declined from Day 1 to Day 2. The calculation t(11) =2.50,p< .05. The alternative forced-choice task significantly declined as well, t(12) = 2.66 p< .05, but not nearly as much as the integrative decisions. In the end complex decision making that asked its participants to integrate multiple matching decisions was impaired by a large margin.

Critique:

The researchers received the answer they were likely expecting. The prefrontal cortex was where the decision making occurred and where the lack of sleep affected the subjects. Overall the experiment was a success and proved to be a significant contribution to research on sleep deprivation and how it affects decision-making. It showed how important sleep is when it comes to making complex decisions on the spot.

Source: Schnyer, D. M., Zeithamova, D., & Williams, V. (2009). Decision-making under conditions of sleep deprivation: Cognitive and neural consequences. Military Psychology, 21(S1), S36. http://cognem.uoregon.edu/files/2014/10/Decision-making-under-conditions-of-sleep-deprivation-Cognitive-and-neural-consequences-152m10o.pdf

Summary of Findings: Prediction Markets (3.5 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in October 2017 regarding Prediction Markets as an Analytic Technique Method specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Prediction markets aggregate collective knowledge of the crowd through the use of a trading based system, allowing participants to invest a stake in a desired outcome they believe will come true. Prediction Markets, when conducted correctly, can provide a range of reasonably accurate forecasts and provide strong reliability to the answered question.

Strengths:

• Can be more efficient than some bureaucratic processes
• Able to aggregate disparate pieces of information to accurately predict resolvable questions
• The incentive to gain profits largely eliminates the occurrence of groupthink
• Works well even when people have limited knowledge about their surrounding environment and the people with whom they transact
• Can incorporate insight from experts across many different fields
• Experts not required

Weaknesses:

• No system for assessing the difficulty of the question
• Doesn’t work well with open ended questions
• Prone to question interpretation gap failures - difficult to determine if question answered actually satisfies intelligence requirement
• Can be manipulated if a speculative trade influences the beliefs of other traders, whether by playing by the rules or creating ideas that cheat the market
• Some moderate level of expertise is required to be a forecaster
• Long term estimates are at risk of forecaster apathy
• Requires a large volume of analysts to create the number of estimates needed

How-To:

1. Choose a resolvable question
2. Gather participants that have a general knowledge of the issue
3. Stipulate incentives
4. Have participants wager on the likely answer, candidate, or outcome
5. Calculate market valuations
6. Evaluate results
7. Inform participants

Application of Technique:

The class received handouts with a list of NFL Football Teams and their standings in 2002 along with $100. They were asked to bet money on which teams they thought would win the SuperBowl in 2003 based on their final standings. After looking at the statistics, students would place bets on the top 3 teams they thought would win. After the bets were placed, we looked at the teams with the most bets and calculated the results. Without a binary choice, the class was capable of making the accurate prediction that the New England Patriots would win the 2003 Super Bowl.

For Further Information:

1. Predictit
2. How Prediction Markets Works
3. Robin Hanson - Idea Futures
4. Philip Tetlock

Using Prediction Markets to Forecast Research Evaluations

By Sam Farnan

Summary

Researchers utilized prediction markets to determine its feasibility with predicting the accuracy of research evaluations. Specifically, they sought to explore if a prediction market model would produce similar results to the Research Excellence Framework of 2014 (REF2014) The REF2014 was six-year process of evaluating research quality in educations in the United Kingdom. REF2014 came under criticism due to the lengthy, costly, and complex way it was conducted.

Researchers in the UK hypothesized whether a prediction market would offer the same results with much less bureaucracy than the REF2014 brought upon academic institutions. For a sample size, they examined 33 chemistry departments within the UK's higher education system. A total of 16 participants aided in the study, and ultimately concluded that in this case, the prediction market actually had less errors overall and showed similar results to the REF2014 as it related to the selected chemistry departments. There were still a number of errors, specifically with regards to institutions sacrificing research quality and ranking to gain research income. 

Critique

I feel the number of participants that the researchers utilized was far too small to implicate that prediction market models could replicate the imperfect, yet expansive, REF2014. Additionally, prediction markets may not account for the more detailed aspects of large evaluations similar to this one as shown in the study above. Although this study shows potential in utilizing prediction markets in this case and had a solid overall design, I believe much more research is needed in order to make a claim that prediction markets are able to reliably replicate results from a large evaluation such as REF2014. 

Source: http://rsos.royalsocietypublishing.org/content/royopensci/2/10/150287.full.pdf

Manipulation in Prediction Markets - Chasing the Fraudsters

Summary by Keith Robinson Jr.

Summary: 

In this article, the researchers address the issues of manipulation and fraud in prediction markets and examine fraud detection approaches. While the authors acknowledge the versatility and forecasting accuracy of prediction markets in comparison to polls or even statistical models, they are not without issues. First, the researchers concluded that to some extent, prediction markets can be manipulated (manipulation defined as a speculative attack that achieves its objective of changing prices). Researchers have come to mixed conclusions regarding manipulation in prediction markets. While anecdotal evidence has shown that the manipulation affects the information aggregation aspect, not reducing predictive accuracy of forecast, other evidence has revealed that "manipulators highly incited for inaccurate predictions, can diminish the predictive power of the markets down to a level that is no better than random guessing" (p. 2981).

Second, the literature looks at fraud in prediction markets. Frequently, extant literature on prediction markets consider traders playing by the rules; however, traders may play by the rules and create other ideas how to cheat the market. They focus on the visualization of the Fraud Cube (see Figure 1.), a framework to understand and uncover where a prediction market may be manipulated or cheated. In order fraud to occur in prediction markets three dimensions must occur: 1) desire/objective (whether to disrupt the market, self-enrichment, or both, 2) temporal horizon (short-term or in the long run - realize quick profits or destroy market prediction or vested interest in the outcome or decisions derived), and 3) source of incentive (the incentive is caused by an inner incentive scheme inside the market or externally).

Figure 1. Framework to understand and uncover where a prediction market may be manipulated or cheated. 

Next, existing fraud detection and trading patterns recognized by Blume et al. (2010) are highlighted. Prominent detection strategies revolve around "ping-pong indicators," focusing on transfer or money, and prominent-edge indicators," taking a look at stocks. Realizing Blume's indicator shortcomings, the researchers developed a simple algorithm, easily applicable for practitioners. This algorithm utilizes a scoring system, scoring traders with "suspicious points" whereas the top ranks have the highest probability to commit prediction market fraud. The researchers evaluate the algorithm during a 12 week data collection encompassing 2,111 participants conducting 112,386 transactions. The algorithm was able to find 484 suspects, 6 traders having more than 200 suspicious points with 551 points being the highest yield.

Critique:

The authors utilized existing literature to simplify a method for application by practitioners. While it is not an end all be all solution, the authors acknowledge their algorithm's own limitations. All in all, the article highlights an under-addressed issue that can damper prediction market accuracy and implications for such.



Source: Kloker, Simon & T. Kranz, Tobias. (2017). Manipulation in Prediction Markets - Chasing the Fraudsters. Retrieved from https://www.researchgate.net/publication/318563054_Manipulation_in_Prediction_Markets_-_Chasing_the_Fraudsters.

Using Prediction Markets to Enhance US Intelligence Capabilities - Puong Fei Yeh

Summary and Critique by Evan Garfield

Summary

 Prediction markets allow participants to stake bets on the likelihood of various events taking place. This method involves the trading of contracts tied to future outcomes.The market price is essentially an estimate of the probability of a future outcome occurring. These market prices reflect the  participants collective confidence that an outcome will occur. In this article the author discusses the value in using prediction markets for the intelligence community. Shes emphasizes that prediction markets are reliable aggregate measures of disparate and dispersed information and can result in forecasts that are more accurate than those of experts. 

According to the author, use of prediction markets in the intelligence community can be traced back to 2001 within the Defense Advanced Research Project Agency (DARPA). DARPA’s Future Markets Applied to Prediction (FutureMAP) program tested the effectiveness of prediction markets in forecasting future events. Under the FutureMap program, the Policy Analysis Market (PAM) offered trading on a variety of different contracts (political, economic, military indicators, etc). The program, however, was very brief and terminated in 2003.

The author continues to discuss the Hayek hypothesis that market prices are the means in which disparate pieces of information are aggregated. According to Hayek, 

"The mere fact that there is one price for any commodity…brings about the solution which…might have been arrived at by one single mind possessing all the information which is, in fact, dispersed among all the people involved in the process"

Going off this point, the author stresses that prediction markets works even when people have limited knowledge about their surrounding environment and the people with whom they transact. She then applies this utility to address needs of the intelligence community. She cites the 9/11 Commission, and its conclusion that lack of unity of effort in information sharing as the biggest impediment to all-source analysis. 

The author then discusses some concerns with prediction markets including market design issues and market manipulation and bias. There are questions with regards to number of traders in a market. Furthermore, Analysts might engage in trading behavior to fit a certain policy outcome. Behavioral bias may also occur when traders trade according to the outcomes they desire rather than a dispassionate assessment of what is likely. She states, "An analogy is that in the run-up to the Iraq war, intelligence analysts were so convinced that Iraq had reconstituted their WMD programs that any evidence, regardless of its veracity, only served to harden their earlier convictions".

Critique

The author does a good job discussing the utility is using prediction markets for strategic intelligence as well as potential concerns when using prediction markets. However, more research is needed with regards to  as whether real-money markets produce better accuracy than play-money markets. Furthermore, more study is needed on whether prediction markets can be applied for tactical analysis. The author also does a good job stressing the utility of prediction markets in promoting collaborative intelligence and enhanced forecasting. The nature of this methodology also allows for non-subject matter experts to contribute without significantly hurting forecasting accuracy. This approach allows for broad contribution across the entire intelligence community and helps reduce the risk of group think. Given the compartmentalization of the intelligence community, prediction markets allow for invaluable aggregation of analyst input for complex issues.


Source: https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/csi-studies/studies/vol50no4/using-prediction-markets-to-enhance-us-intelligence-capabilities.html