COVID-19 Pandemic

Hello everyone! Think of this space as a crowdsourced notebook . . . everyone is welcome to take and share DEMS3706 lecture and reading notes here. -[;.

Module One - Rational, Irrational, or Something Else? 2 Cognitive Biases - Definitions 2 Bounded Rationality (Tversky, Kahneman) 6 Representativeness 6 Availability Bias 7 Adjustment and Anchoring 8 Cultural Cognition (Kahan, Braman) 8 DEMS3706 Lecture #1 10 DEMS3706 Lecture #2 (Cultural Cognition) 11 Module Two - Uncertainty & Prediction 13 Prediction, Cognition and the Brain (Bubic, von Cramon, Schubotz) 13 “A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts? (Gigerenzer et al.) 16 Don’t Believe the COVID-19 Models (Tufekci) 18 Lecture #1 20 Lecture #2 21 Module Three - Fear, Anxiety, and All Things Scary 25 Lecture #1 25 Module Four - Decision-making Under Pressure 29 Lecture #1 29 Module Five - Expertise & Thinking as an Institution 33 54Lecture #1 33 Module Six - PTSD & Mental Health 35 Disasters and Humans (DEMS3706 SU2020, Dr. Eric Kennedy) 1

Here are two images of cognitive biases of the ones that are required from the reading guide. The examples are simple and easy to follow:

Many decisions are based on beliefs on the probability of uncertain events.

People rely on a limited number of heuristic principles which reduce complex tasks of assessing probability to simple judgements.

Many probabilistic questions take one of the following forms:

· What is the probability that object A belongs to class B?

· What is the probability that event A originates from process B?

· What is the probability that process B will generate event A?

People typically rely on the representativeness heuristic: the probability is evaluated by the degree to which A resembles (is representative of) B.

“Steve is quiet. What’s Steve’s job? Steve is probably a librarian.”

This can cause insensitivity to the prior probability of outcomes.

The prior probability/base-rate frequency is ignored in favour of representativeness.

“Most of the people in town are farmers, but Steve is quiet so he must be a librarian.”

The heuristic can cause insensitivity to sample size.

Probability shows the similarity of a sample statistic to a population parameter is lower when the sample size is small. The heuristic ignores this fact.

· “The probability of getting 60% heads when tossing coins is the same whether you toss a coin 10 times or 1000 times.”

The heuristic can cause people to expect a sequence based on the essential characteristics of a random process to be locally representative of the process.

“The sequence HTHTTH is more likely than HHHTTT or HHHHTH”

Gambler’s fallacy is an example of this expectation.

The heuristic can cause insensitivity to predictability, that is, one ignores how much predictive power a piece of information has when making a prediction, instead.

· Ex. “Eric Kennedy did a guest lecture for one of my courses once and I was very impressed, therefore Eric Kennedy must be the best professor at York.“

A good fit between the predicted outcome and the input information may be called the illusion of validity.

The internal consistency of a pattern of inputs is a major determinant of one's confidence in predictions based on these inputs.

This illusion persists even when the judge is aware of the factors that limit the accuracy of his predictions.

The heuristic causes misconceptions of regressions to the mean.

Consider two random variables X and Y which have the same distribution. If one selects individuals whose average X score deviates from the mean of X by k units, then the average of their Y scores will usually deviate from the mean of Y by less than k units.

This statistical phenomenon is explained with all sorts of cargo cult thinking.

The probability of an event is assessed by the ease of which instances or occurrences can be brought to mind.

“I can think of many terrorist attacks that happened in Toronto, therefore terrorism is a major risk in Toronto.”

Biases due to the retrievability of instances: larger, more frequent classes of outcomes may be recalled more easily than less frequent classes, but other factors also affect recall.

The size of a class is judged by the availability of its instances.

A class whose instances are easily retrieved will appear more numerous than a class of equal frequency whose instances are less retrievable.

Biases due to the effectiveness of a search set: if the method of retrieving instances of one class is more effective than of another, the first will appear to be more frequent.

“There are more words that start with ‘r’ than those that have ‘r’ as the third letter.”

Biases of imaginability: if the instances of a class are generated rather than stored in memory, the more easily imagined class is perceived to be more frequent.

“There are more groups of 2 that can be constructed from 100 people than there are groups of 8.”

Illusory correlation: the co-occurrence of events is overestimated when the events are associated.

“Suspiciousness is seen in the eyes. When this man draws pictures, the eyes are weird. He must be suspicious.”

People typically start an estimate from an initial value then make adjustments. These adjustments are insufficient.

Biases in the evaluation of conjunctive and disjunctive events: the probability of conjunctive events are overestimated and the probability of disjunctive events is underestimated.

“A project with many consecutive steps, each one depending on the previous is likely to succeed.”

“A system with many critical components, with the failure of any individual component causing the entire system to fail, is not unsafe.”

· People agree that well-being is good, but disagree on what generates well-being

· It is naive to assume poor education causes this disagreement

· Yes, empirical proof often requires high technical skill to understand

· But there often isn’t even a consensus to believe

· Opinions correlate to membership in a variety of social groups, even among experts

· Beliefs cluster (gun control opponents often don’t believe in global warming)

· Cultural cognition: the psychological disposition of persons to conform their factual beliefs about the instrumental efficacy (or perversity) of law to their cultural evaluations of the activities subject to regulation.

· Cultural beliefs are prior to factual beliefs

· People like policies that line up with their cultural values

· What people think the about the consequences of policies derive from their cultural worldviews

· We can’t all be experts, so this is what we do ¯\_(ツ)_/¯

· People have cultural worldviews that follow the dimensions of “group” and “grid” proposed by Douglas and Wildavsky in Risk and Culture

· Group: Individualist (Low) vs Solidarist/Communitarian (High)

· Collective responsibility vs individual responsibility

· Grid: Egalitarian (Low) vs Hierarchist (High)

· Social distribution vs hierarchical distribution

· Individuals select certain risks for attention and disregard others in a way that reflects and reinforces the particular worldviews to which they adhere

· On the environment:

· Egalitarians, solidarists: Reducing environmental risk justifies regulating commercial activities that are productive of social inequality, legitimize unconstrained self-interest, therefore it exists

· Individualists: The existence of environmental risk threatens market autonomy and other private orderings, therefore it doesn’t exist

· Hierarchists: The existence of environmental risk would mean the social and governmental elites are incompetent, therefore it doesn’t exist

· Douglas wrote in Purity and Danger that morality, defined by culture, prescribes what causes danger

· Don’t commit adultery or incest, don’t disrespect or challenge your rulers

· Disgust and revulsion are strongly connected to contravention of ordered relations, contradiction of cherished classifications

· Why do we have cultural cognition?

· The nexus between danger and morality justifies the accepted system of morality

· We need to know what actions and states promote our interests

· Cultural dissonance avoidance: It’s uncomfortable to have beliefs about what’s harmful and benign that contradict commitments and affiliations essential to one’s identity

· Affect: Perceptions of how harmful activities are informed by their affect, the visceral reactions those activities trigger, largely determined by culture.

· In-group/out-group dynamics: We trust people like us

· Group polarization: We want to avoid censure for opposing opinions, so we agree with the dominant opinion even if we don’t agree all that much

· Naive realism = individuals tend to believe that the opinions/beliefs/values held by their own cultural group were reached via objective assessment whereas the beliefs held by opposing cultural groups are based on biased information sources and influenced by their worldviews. Therefore, evidence and truth can never be transmitted across borders because each side thinks the others’ sources are biased.

· Reactive Devaluation = individuals who belong to one cultural group downplay and even dismiss the persuasiveness of the opposing group’s arguments and evidence, especially in when intergroup conflict takes place.

· What should we do about it?

· Cultural cognition diminishes our ability to integrate reliable information or even identify it

· Scientists aren’t immune to it

· We should make policies palatable to all cultural sides

· Air France

· the problem here is not the plane, the problem is that in the minutes that followed the computer glitch, the pilots experienced serious confusion, lack of orientation, lack of awareness. This disorientation caused the pilots to stall the plane; this decision is what ultimately caused the fatal plane crash

· Sensory confusion leads to incorrect inputs . . . this is why we should pay attention to cognitive biases

· Different Types of Illusions

· Luminance and contrast (dark spots appear at the intersection of white lines) = this illusion is based on the contrast of light and dark colours and plays with the way our brain processes colours and light.

· Geometric/angle illusions

· Motion

· Impossible Figures

· Size consistency

· Basically, brains are constantly searching for ways to process information more quickly and easily, and we continue to make predictable mistakes because we take shortcuts which cause us to incorrectly interpret information

· Tversky & Kahneman

· Bounded Rationality = in order to process the overwhelming amounts of information that we face daily, our brains use shortcuts or heuristics

· Loss aversion = we tend to prefer avoiding losses to acquiring equivalent gains

· We are less likely to take chances if we perceive a loss will result from our actions; we are more likely to take a chance if we perceive our chance-taking will result in some positive gain

· There is the way we should think of something (mathematically) and the way we do think of things (heuristically)

· Representativeness

· Our probability estimations are influenced by how closely an example (let’s say person) matches our pre-existing stereotypes. EG. the probability that Steve is a librarian seems higher because he sounds like the stereotypical librarian

· Cass Sunstein

· Nudges: By identifying and understanding cognitive heuristics, you can harness the power of these biases and use them to elicit positive behaviour (or, in the case of corporations, elicit corporate-friendly behaviours)

· Kahneman and Tversky’s system 1 vs system 2 thinking

· System 1 - rapid and subconscious thinking

· System 2 - formal, logical and mathematical thinking

· Kahneman and Tversky's theory is that, even when we think we're using the slow, methodological, logical system two mode - we're often being tricked by system one mistakes.

· Optical illusions, cognitive biases and heuristics (optical illusions for our brains/ shortcuts our brains take)

· Bounded rationality = we don’t possess the bandwidth to process all stimuli and information rationally

·

· Beliefs vs Actions

· Belief disagreements pertain to beliefs rather than actions . . . people disagree about what to believe

· Action disagreements pertain to actions . . . people disagree about what to do in a given situation and that is when dangerous consequences can occur (because inaction is as much of a decision as action)

· So why do disagreements occur?

· Theory 1: Disagreement exists because people are uniformed.

· We disagree because I am well versed in the topic, well read, well informed and the disagreer is not.

· We often associate disagreement with the belief that “they are wrong” or use fundamental attribution error/out-group homogeneity effect

· Theory 2: Affective Preferences

· Some of our preferences and beliefs are influenced by our past experiences, how we feel when thinking of a certain belief or value

· Our feelings and past experiences can bleed into our beliefs and actions

· But this theory only explains light, affective disagreements; it doesn’t really explain substantial, significant disagreements

· Theory 3: Upbringing

· Perhaps our beliefs are influenced by how we were raised/the things we are taught to believe (after all, our parent’s beliefs are strong predictors of our religions, politics, etc)

· However, we possess different beliefs than our parents and our parents don’t teach us about everything - so how do we form beliefs that are not directly related to our parents, upbringing?

· Theory 4: Lack of Education?

· Perhaps those who disagree just don’t possess enough information? Links to Theory 1.

· However, evidence suggests that the more education one receives, the greater the degree of convergence between people’s beliefs. In other words, two people tend to disagree MORE as their education increases.

· This finding indicates that confirmation bias plays a role in education

·

· Cultural Cognition

· Kahan and Braman argue that people disagree because of cultural cognition . . . people hold the same goals, but they disagree about the best way to achieve these goals

· K and B posit that beliefs travel as a pack - beliefs follow an identifiable pattern. That is, opinions regarding controversial issues such as climate change, abortion, and gun control are linked, despite that they are unrelated

· Belief patterns do not occur by chance. We have prior beliefs and prior values that shape what we believe.

· We do not approach issues as blank slates. We approach issues with our pre-existing set of values and worldviews and make decisions based on what solutions best fit with our current beliefs.

· Why does this happen? We cannot answer big, controversial questions by ourselves - we cannot gather sufficient proof independently. Subsequently, to answer these questions, we have to trust experts/take the word of people we trust. And we tend to trust the experts we know of/are exposed to - we are familiar with the experts we CHOOSE to watch and those we choose to watch are often those that share our values. In other words, we trust the people that share our values.

· EG. If you choose to watch Fox News, you are more likely to trust the experts featured on Fox News.

· But how do we know who is “like us”? The answer lies in groups and grids.

· Group (individualistic vs communitarian)

· Individualistic = singular, value being able to take care of yourself

· Communitarian = concerned with the well being of the whole, value a system that cares for others,

· Grid (who ought to get what -- egalitarian vs hierarchical)

· Egalitarian (low grid) = everyone should have access to the same resources

Grid called grid because it is related to levels of hierarchy. Group is called group because it refers to those in our circle - do we see ourselves as part of a wider community or as a singular individual

· Individualist/Hierarchical = Republicans and Conservatives

· Egalitarian/Communitarian = NDP

· Egal/Individ = Green Party

· Values turn into beliefs

· Biased Assimilation = you accept information and view it as reliable if it matches with your previous beliefs -- explains confirmation bias

· Naive realism = individuals attribute their beliefs to research and objective assessment whereas they view other people’s beliefs as biased

· Reactive devaluation = dismiss valid information/evidence if it presented by the “other” group

Last week, we learned why we don’t always perceive things the way they actually are -- why our senses deceive us. This week, we explain how our flawed perceptions impact our beliefs

· Prediction/Predictive Processing - Any type of processing that incorporates or generates information about future states of the body or environment

·

· Dimensions of Prediction

·

· Type (probabilistic, deterministic)

· Specificity (low, high)

· Level (explicit, implicit)

· Domain (motor, perceptual, cognitive)

· Timescale (long, short)

· Events can be predictable if they occur in a non-random fashion allowing the brain to find a deterministic or probabilistic regularity to the relationship between different events

· The brain may still attempt to predict if the input is random

· The brain may use analogies if the input is novel

· In non-random contexts predictions are generated by learning and identifying associations, esp. temporal associations

· Accumulate info about statistical regularities while dealing with noise and uncertainty

· Applying inference rules, analogies

· Concrete/first-order rules - Repetition of stimulus triggers an expectation of continuation of its appearance

· Higher-order rules - Specific non-interchangeable stimuli, more complex events, different contexts

· Serial order processing

· Linear/flat sequences (local dependencies)

· Non-linear/hierarchical sequences (long-distance dependencies)

· Spatial or abstract associations can also be used (contextual frames) to help identify stimuli

· Recombining past events creates more complex predictions, “memories of the future”

· Anticipation/Preparation/Predictive Coding - Formulating short-term expectations and communicating them to sensory or motor areas

· Elevated levels of processing in sensory or motor areas of the brain prior to and facilitating the processing of an expected perceptual or motor event (David LaBerge); impact of predictions on current behaviour (Martin V. Butz et al.); Mapping of causes to sensory expressions (Karl Friston)

· Levels

· Explicit (Representations of future states)

· Implicit (Behaviours or habits)

· Explicit is more likely to predict upcoming stimulus before it appears

· Expectation/Prediction/Prospective Code - Representation of what is predicted to occur in the future

· An item stored in working or long-term memory including information about the spatial and temporal characteristics of an expected event

· May be abstract or verbal which does not necessarily pre-activate relevant sensory cortices

· Prospective Code - Representations of present events which contain info about their future effects or goals

· Timescales

· Formulation

· Based on long-term experience

· Based on short-term exposure to non-random patterns

· Event distance

· Short-term/online

· Ongoing behaviour, motor control, more accurate

· Long-term/offline

· Not immediately relevant, less accurate

· Multiple expectations with different time/space can be made for the same event across different brain systems

· Prospection - Consideration of potential distant future events

· Ability to pre-experience the future by simulating it in our minds (Gilbert and Wilson); stored information being used to imagine, simulate, and predict future events (Daniel Schacter)

· May lack the detail of genuine perception; shortened, essentialized

· May not be realistic or reliable; prone to error

· Based on exemplars of a specific scenario, but run in a decontextualized, comparative manner

· Benefits of prediction

· Shortens perception time

· Save resources

· Prepare reactions

· Faster recognition and interpretation by limiting potential responses to environment

· Increased accuracy, speed, maintenance of info processing

· Creates coherent, stable representations of environment

· Guide top-down deployment of attention, improve info seeking, decision-making

· Trigger and guide behaviour

· Prospective codes

· Actions are preceded by response-related anticipation, voluntary behaviour is controlled by a representation of its outcomes (goal-directed behaviour)

· Could also be facilitated by expected emotional consequences of actions, which are used as the basis of additional predictions

· Prediction is a “bias signal” that improves the computational efficiency of specific areas

· Expected stimuli (matches) are processed more efficiently than unexpected stimuli (mismatches)

· However, mismatches have higher relevancy and priority

· They are more valuable since they signal unsuccessful learning

· They cost more attention (less efficient)

· Must check behavioural relevance

· Relevant to the current mental set?

· Environmental noise?

· If relevant and informative, update knowledge, adapt behaviour

· Novel events, environment changes

· Prediction allows us to direct our behavior towards the future, while remaining well grounded and guided by the information pertaining to the present and the past.

· In 1980 a study of college students by Murphy et al. found people misunderstand the meaning of probabilistic statements like “a precipitation probability forecast of 30%”

· It wasn’t a misunderstanding of probabilities, but a misunderstanding of “the event to which the probabilities refer”

· This study examined how the public in five different countries understood such probabilistic statements

· The paper also argues that the confusion is due to not knowing the reference class to which the statement refers

· A forecast such as “There is a 30% chance of rain tomorrow” conveys a single-event probability, which, by definition, does not specify the class of events to which it refers

· In view of this ambiguity, the public will likely interpret the statement by attaching more than one reference class to probabilities of rain, and not necessarily the class intended by meteorologists.

· Consequently, laypeople may interpret a probability of rain very differently than intended by experts.

· A psychiatrist who prescribed Prozac to depressed patients used to inform them that they had a 30–50% chance of developing a sexual problem such as impotence or loss of sexual interest

· Many patients became concerned and anxious.

· Telling patients that out of every 10 people to whom he prescribes Prozac, three to five experience sexual problems seemed to put patients more at ease

· Many had thought that something would go awry in 30–50% of their sexual encounters

· The original approach to risk communication left the reference class unclear

· When risks are solely communicated in terms of single-event probabilities, people have little choice but to fill in a class spontaneously, based on their own perspective on the situation

· The National Weather Service defines the probability of precipitation as “the likelihood of occurrence (expressed as a percentage) of a measurable amount of liquid precipitation...during a specified period of time at any given point in the forecast area”

· In practice, this means the rain forecast is the percentage correct of those days when rain was forecast

· 30% chance of rain does not mean that it will rain tomorrow in 30% of the area or 30% of the time.

· Hypotheses:

· The public has no common understanding of what a probability of rain means

· The confusion should be lower and the prevalence of the days interpretation should be higher

· among people in countries that have been exposed to probabilistic weather

· among people who have been exposed to probabilistic weather forecasts for a larger proportion of their lives

· Probabilities of rain were introduced into mass media weather forecasts in:

· New York in 1965

· Amsterdam in 1975

· Berlin in the late 1980s

· Milan only on the Internet

· Athens, never

· The prevalence of the days interpretation in the five countries is not positively correlated with length of national exposure

· Consistent with the individual-exposure hypothesis, the proportion of individual exposure was positively related to choosing the days interpretation

· Individual exposure as in proportion of each participant’s life during which he or she had been exposed to weather forecasts expressed in probabilistic terms

· A few respondents to the open question referred to classes of events other than area or time

· The inclusion of quantitative probabilities in weather forecasts has been advocated because probabilities can “express the uncertainty inherent in forecasts in a precise, unambiguous manner, whereas...traditional forecast terminology is subject to...misinterpretations”

· If probabilities are really unambiguous, one may ask why probabilistic forecasts are still so widely misunderstood.

· In countries such as Greece, probabilities of rain are simply not provided to the public

· This also holds to some degree in other countries, where only some mass media use probabilities.

· When probabilistic weather forecasts are provided, they are typically presented without explaining what class of events they refer to.

· Third, in the rare cases where an explanation is presented, it sometimes specifies the wrong reference class. (e.g. Netherlands)

· The authors suggest misunderstandings can be easily reduced if a statement specifying the intended reference class is added.

· Quantitative probabilities will continue to confuse the public as long as experts do not spell out the reference class when they communicate with the public.

· Epidemiologists routinely turn to models to predict the progression of an infectious disease.

· The Trump administration has just released the model for the trajectory of the COVID-19 pandemic in America.

· We can expect a lot of back-and-forth about whether its mortality estimates are too high or low.

· There is no right answer.

· Right answers are not what epidemiological models are for.

· Sometimes, when we succeed it looks like we overreacted.

· A near miss can make a model look false.

· But that’s not always what happened.

· It just means we won.

· Fighting public suspicion of these models is as old as modern epidemiology

· John Snow’s famous cholera maps in 1854 proved London’s cholera was spreading through water that came out of pumps, not the city’s foul-smelling air

· Many people didn’t believe Snow, because they lived in a world without a clear understanding of germ theory and only the most rudimentary microscopes.

· In our time, however, the problem is sometimes that people believe epidemiologists, and then get mad when their models aren’t crystal balls.

· When an epidemiological model is believed and acted on, it can look like it was false.

· Epidemiologists also have to estimate the impact of interventions like social isolation.

· Limited, perhaps censored data

· Can we trust China?

· Differing interventions

· (e.g. China entire families quarantined vs US quarantines)

· These models describe a range of possibilities

· The variety of potential outcomes coming from a single epidemiological model may seem extreme and even counterintuitive

· Those possibilities are highly sensitive to our actions

· Epidemics are especially sensitive to initial inputs and timing

· Where should those parameters come from?

· Model-makers have to work with the data they have, yet a novel virus has a lot of unknowns.

· Epidemics grow exponentially

· A model’s robustness depends on how often it gets tried out and tweaked based on data and its performance.

· Trump was projected to lose in most US election polls

· Only 2 elections before 2016 occurred with Facebook

· Bad models or the less likely outcome naturally occurred?

· With this novel coronavirus, there are a lot of things we don’t know because we’ve never tested our models, and we have no way to do so.

· Why should we use models if they’re not certain?

· Epidemiology gives us agency to identify and calibrate our actions by pruning catastrophic branches of a tree of possibilities that lies before us.

· Epidemiological models have “tails”—the extreme ends of the probability spectrum.

· Think of those tails as branches in a decision tree.

· At the beginning of a pandemic, we have the disadvantage of higher uncertainty, but the advantage of lower costs to actions

· The disease is less widespread.

· By acting we change the underlying parameters

· The U.K. had almost no social-isolation measures in place, to let the virus run its course through the population, with the exception of the elderly to create “herd immunity.”

· Let enough people get sick and recover from the mild version of the disease

· An epidemiological model from Imperial College London projected that without drastic interventions, more than half a million Britons would die from COVID-19

· The stark numbers prompted British Prime Minister Boris Johnson to change course, shutting down public life and ordering the population to stay at home.

· Neil Ferguson, the scientist who led the Imperial College team, testified before Parliament that he expected deaths in the U.K. to top out at about 20,000

· This caused media outrage

· One former New York Times reporter described it as “a remarkable turn,”

· The British tabloid the Daily Mail ran a story about how the scientist had a “patchy” record in modeling.

· The conservative site The Federalist even declared, “The Scientist Whose Doomsday Pandemic Model Predicted Armageddon Just Walked Back the Apocalyptic Predictions.”

· There was no turn in the model.

· The model lays out a range of predictions from tens of thousands to 500,000 dead which all depend on how people react.

· The spread of the disease depends on exactly when you stop cases from doubling

· Even a few days can make an enormous difference.

· Lombardy and Veneto, took different approaches to the community spread of the epidemic.

· Both mandated social distancing, but only Veneto undertook massive contact tracing and testing early on.

· Lombardy is now tragically overrun with the disease, while Veneto has managed to mostly contain the epidemic

· The model worked with limited parameters and data

· Imperial College model uses numbers from Wuhan, China, along with some early data from Italy

· Many of these data are not yet settled, and many questions remain

· What’s the attack rate—the number of people who get infected within an exposed group, like a household?

· Do people who recover have immunity?

· How widespread are asymptomatic cases, and how infectious are they?

· Are there super-spreaders?

· What are the false positive and false negative rates of our tests?

· Etc

· some predictions are so vast and overwhelming/impossible

· Prediction is composed of different components and we must pull apart different parts of prediction to make a forecast about the future

· Anticipation = things that are short-term, relatively simple, and closely connected to our sense and motor skills (eg. seeing a visual pattern

· Prospection = long-term, abstract prediction . . . about long-term events that lack motor sensory (more theoretical things that lack clear patterns). EG. when will the second wave of COVID-19 occur

· Anticipation is the process of forming an expectation

· Expectation is very sense based, what we generate from anticipation and prospection. Anticipation and prospection are the processes and expectation is the result

· There are many brain regions involved in making predictions and different brain regions light up when making predictions. Lots of involvement from prefrontal cortex

· Prediction is not located in one part of your brain, it involves many areas of your brain

· The parts of the brain involved in prediction are also used in other tasks

· Blurred line . . . all parts work together, there is not just one part of the brain that handles prediction

· Our brains have evolved to handle some predictions very well (linear predictions) and some predictions pretty poorly (exponential predictions)

· Bad at handling exponential predictions because we haven’t been exposed to exponential changes as much . . . we are biased toward linear patterns and predictions, we simplify things down and get fixated on one specific aspect or component of our prediction (we create certainty where certainty doesn’t exist)

· When we see an event over and over again, our neural connections get stronger and stronger (if you have experienced hurricanes before and have always gotten by without evacuating, you will tend not to evacuate when faced with a hurricane warning)

· neural connections that make up the memory of an event make you think that this future scenario will follow past scenarios

· Our brains are good at handling short-term predictions because evolutionarily, we have had hundreds of thousands of years of practice making short-term predictions/looking at patterns

· Long-term predictions involve looking at situations that will break the pattern/deviate from the linear pattern

· Prediction can inform an individual’s future behaviour, prediction can also influence at the organizational level

· Prediction is useful because it helps us get a picture of what is occurring/it informs us

· Prediction reduces anxiety and gives us clarity and relief from fear, helping move things from the general realm of anxiety to specific fear/focused anxiety.

· Prediction allows us to do stuff in the future, and it also reduces cognitive load by reducing stress

· Risk = probability x consequence

· Probability = how likely it is that something occurs

· Consequence = the impact of that event if it occurs

· Therefore, predicting risk requires you to predict the elements of risk

· 30% Chance of Rain (misunderstanding of reference classes)

· People often misunderstand the reference class a prediction is about

· A reference class is the event that the prediction refers to

· When people see weather forecasts, they frequently misinterpret the forecast statement and apply the probability to all different classes

· Theory states that people misunderstand predictions because they misunderstand the reference class this

· Predictions are powerful because they simplify things. We feel like we have control over our anxieties

· Imaginaries are complex expectations about the future, often shared by many people, regarding norms, values, institutions, and possibilities

· Pandemics are controllable and predictable vs pandemics are divine punishment

· Imaginaries are composed by several sub-predictions and sub-beliefs, so imaginaries depend on assumptions and beliefs about what will occur

· They are essentially meta-predictions (a prediction about lots of other predictions)

· Imaginaries are influenced by our group-grid status and influence what we think is possible, what we worry about and what we don’t, and how much control we think we have

· Imaginaries tend to be shared by some people (people working in public health) but not others (people who don’t think the virus is a big deal)

· Google prediction algorithm holds an imaginary that is different than the individual “googler’s” imaginary - Google predicts the individual is ill, individual might just be

· Optional Reading: Pandemic Prophecies - How we construct imaginaries

· Optional Reading: Algorithms and Google Flu Trends - Creators of Google Flu Trends have diff. imaginaries than users, which makes it break

· Optional Reading: Simulation and expertise in bushfire prediction - Experts have certain ways of building imaginaries

· Thinking about the future involves more brain regions and complexity than the present since it involves simulation

· But it’s not all that different

· Prediction involves simplification

· Prospection is different from anticipation

· Dimensions of prediction

· We don’t know much about how correction happens or how the different regions of the brain work together

· Cultural cognition and other biases shape our reference classes, imaginaries and other parts of our predictions

· E.g. Trump administration: Only x number of people will die from COVID-19, but we’re framing it as y number of people would’ve died

· Imaginaries are from sociology

Fear:

· an emotional or affective response to a situation

· Fear is a reaction, rather than a thing itself

· Fear is an emotion that is caused by a perceived threat

· Often connected to behavioural responses like fight, flight, freeze response

· Fear, like other cognitive processes, has an evolutionary purpose, it helps us stay alive

· Fear is temporary, happens just when the fear trigger is activated

· Fear triggers biophysical reactions

· Fear is not the same as risk . . . our biases and bounded rationality will affect what fears are more salient or significant to us

· Perceived risk may or may not line up with objective risk

What Fears Exist With Respect to C-19?

What Distinguishes Fear?

· Compared to fear, a phobia is seen as an “irrational” or “overactive” fear response . . . a fear response that doesn’t make sense and exists for no beneficial or apparent reason

· Anxiety is more generalized feeling of dread . . . longer lasting than fear (fear response is not just triggered when you are in a situation but also any time you think of that situation), future-oriented, more diffuse threat (not a focused threat but a general threat -- dogs vs any animal), creates excessive caution and avoidance of situation (whereas fear often leads to coping strategies)

Social construction

· Some things are decided by society, we as a society decide what is considered a fear and what is deemed a phobia based on what we see as rational and what fear we see as irrational.

· Fear vs phobia isn’t preordained, the difference between the two is decided by societal perceptions

· We as a society decide what is a rational fear vs an irrational phobia

From Anxiety to Fear

· Something can switch from being a fear to an anxiety or vice versa (anxiety to fear)

· Sometimes this is done explicitly

· Capitalizing on an anxiety to cause a desired change . . . take an anxiety and turn it into a specific fear that help achieve a purpose (blaming a disaster or incident on an ethnic group will cause fear of that ethnic group)

· Scapegoating . . . turn a big anxiety and manipulate it into a fear about a specific ethnic group

· Can also take a fear and manipulate it into a big anxiety . . .

· EG. environmental groups may take a specific fear (fear of a terrorist breaking into a nuclear plant and using the plant for negative means) and try to turn it into a general anxiety about anything nuclear (so now we want to avoid nuclear at all costs)

· Innate vs Learned Fear (a spectrum, not binary)

· Some fears are relatively common across all humans and society (eg. disasters, tornadoes, earthquakes) . . . these are considered “innate fears” because most people possess these fears

· Learned fears are fears that have developed through particular experiences. Eg. a traumatizing childhood experience results in development of a fear

· A personal experience that triggers a fear in a particular individual

· Little Albert Experiment: exposed an infant to an array of different stimuli to gauge his reaction. Then linked stimuli to noise . . . white rat and scary sounds. This is an example of a learned feared.

· Fear of dying is an innate fear, but the degree to which we fear particular causes of sickness or death varies.

Fears are Historically Situated

· Fears have changed throughout history

· Some fears are no longer relevant (or as relevant) because we have found solutions to them . . . eg. polio or smallpox

· New stimuli can create new manifestations of fear (eg. fear of flying was created due to creation of airplane)

· Some groups try to intentionally create fears to advance their purposes (government tries to amplify fear of terrorism because that will allow them to execute certain goals . . . more funding for defence, airport security, etc)

Normalization and Acclimatization (researchers use these terms synonymously)

· Fears can also change at the individual level

· Normalization occurs when we become less fearful of certain stimuli. Through prolonged exposure, you subconsciously become trained to not fear that stimuli

· Often caused by prolonged exposure

· Results in less acute stress reactions

· Doesn’t mean the stress disappears (peak stress subsides,

Measuring fear

· There are two types of definitions . . . conceptual and operational

· Operational definitions are linked to measurement. Define something in a way that allows you to MEASURE it. Define fear in a measurable way.

· Conceptual definitions are just theory/words

Fear Appeals

· We can use fear to motivate change

·

Protection Motivation theory

· Evaluate threat (how much of a risk is this), evaluate coping mechanisms (how would I cope with this), and evaluate rewards of coping with threat and costs of not coping with threat to determine to what degree we will allow the fear to guide our decision

·

· Use first person statements (in this reflection, I will show that this theory caused this phenomenon)

· Clear use of theory statement

· Have you thought of other perspectives? Have you anticipated objections?

· Give a clear definition of a theory and choose one aspect of it as a whole

· Choose an aspect of RPDM (the comparison to past experience types, how this is contrasted with making explicit choices)

· Use shorter quotations (single out a few words rather than a long winded quotes)

· Use quotes from the theory

· Change and adapt as things go

· Don’t commit to your initial thesis or plan

· “I started with automation bias, but I actually wanted to talk about confirmation bias”

· The granularity of the aspect of COVID-19 you use in your reflections should be like “mask-wearing,” “vaccine hesitancy,” etc.

· Use news sources or even /r/coronavirus on Reddit for inspiration

· Acute stress vs chronic stress

· Acute stress

· Flight/fight/freeze

· Evolutionary basis

· If we didn’t take one of these actions, we were less likely to survive a sudden threat

· Fight - stop the threat

· Flight - run away

· Freeze - don’t be seen

· Freeze is a newer element/newer response. We haven’t studied it as much because it does not align with our understanding of threat response, does not adhere to our understanding of physical responses to threats

· Amygdala

· Controls limbic system

· Emotions

· Learning

· Memory

· Feeds into the hypothalamus

· Hypothalamus

· Controls autonomic nervous system

· Unconscious activities

· Breathing

· Heart rate

· BP

· Blood vessels

· 3 branches of ANS

· Enteric nervous system

· Eating, digestion

· Sympathetic nervous system

· Quick mobilization

· Heart races, eyes dilate

· Adrenaline

· [See slides]

· “Speed up”

· Parasympathetic nervous system

· Rest, digestion

· “Slow down”

· Shock

· Two meanings: Medical shock, and Acute Stress Reaction (ASR) “shock”

· Shock

· Insufficient blood flow to body

· ASR

· Increased heart rate

· Sweating

· [See slides]

· Everyone feels ASR

· Even emergency responders

· Train emergency responders to control their stress responses and reduce severity of stress and ASR symptoms via repeated exposure, checklists and procedures (guidance/having a plan/and slowing down reduces stress)

· How do people behave under stress?

· How do we find out?

· [See slides]

· Ask people

· Unreliable, biased

· Planning bias, etc.

· “Oh, I’ll be calm, then I’ll do a kung-fu kick and save everyone”

· Modelling

· Mathematical computer simulations

· But model might not be accurate to real life

· Drills/simulations

· High fidelity simulation

· Desensitization achieved with drills

· Study past disasters

· Excessive drills might desensitize people to a real emergency

· Hotel evac experiment

· Volunteers chosen for fake evac, around 40-50

· Volunteers drilled evac

· Then taken to a hotel as a “reward” which was the actual experiment

· Simulated smoke

· Very early morning

· What did we learn from these experiments?

· Not everyone will evac

· If someone personally tells you to evac, your chance increases

· E.g. police or fire knock on your door

· Factors in evac rate

· Perceived threat

· Frq. false alarms

· Difficulty of evac

· Responsibility for dependents

· The more responsibility, the less likely

· Specificity

· More info = perceived more severe

· More general is less effective

· Perceived accuracy of past warnings

· People don’t drop everything and evac

· They gather belongings, etc.

· Sudden evacuations

· People will act irrationally under stress

· 2016 Fort Mac fires

· Take wrong evac routes

· Take inappropriate/bizzare items

· No clothes, but bear head mantlepiece

· Half a blender and a watermelon

· Garbage bin

· Bag of potatoes

· Read the reading guide this module, the theories are stated within

· Moodle forum is open

· Slides include some theories not covered this lecture

· Goal seduction

· Salience

· Reliability of tests . . . if the actual emergency differs from the tests (eg. tests don’t have smoke, extreme heat, etc.), the test might not give an accurate estimation of how people react.

· Goal seduction . . . get so focused on one goal that you forget/lose sight of other information

· Can be explicit = people take on more risk because they believe they have to take risks in order to achieve goal

· Subconscious = sympathetic response and adrenaline causes you to forget obvious information (get a call to fight a fire and you forget to turn the stove off)

· Making your reading reflections go from A to A+

· Clear use of the theories

· Refuting rebuttals

· Succinctness

· Every sentence should have a purpose and meaning

· Some creativity

· Salience

· How pronounced something is

· A cyclist is more conspicuous wearing hi-vis than all black

· Inattention blindness

· Missing something in plain sight

· E.g. a driver sees an obstacle but hits it anyways, a nurse retrieves a neuromuscular blocking agent instead of an antibiotic, reads the label, and administers it anyways killing the patient

· “Reading” vs reading

· System 1 vs System 2 thinking

· We need to filter noise from relevant info

· Conspicuousness vs salience

· Conspicuousness - “How much something jumps out”

· Conspicuousness is diminished if we are fatigued, overloaded with tasks, have reduced capacity (alcohol, age)

· Sensory conspicuousness (brightness, shape, is the font big, does it stand out to our senses)

· Cognitively conspicuous (cocktail party effect)

· Purposeful attention (you are looking for something in particular so you see that thing more easily)

· Salience - “How important it is”, is the information judged as being relevant

· Theories of expertise development

· Credentials and training (formal courses, apprenticeship

· Experience (number of hours, number of experiences, range of experiences)

· Socialization (develop expertise by nature of who you spend time with, who you speak with . . . Learning how to think, debate, exchange ideas)

· 10,000 Hours (practice makes an expert . . . experts tend to demonstrate a lot of experience doing something, specifically 10,000 hours of experience. However, the raw number of hours does not provide a full picture of expertise . . . expertise depends on mentorship, if you are practicing in a meaningful way, etc.)

· Linear Development (Dreyfus) . . . Expertise consists of a series of stages (1) Novice, (2) Advanced Beginner, (3) Competence, (4) Proficient, and (5) Expert.

· Experience and Socialization (Collins and Evans) . . . spending time around OTHER experts is important to becoming an expert because it gives you the tacit or implicit knowledge that cannot be explained explicitly. EG. nurses/doctors can’t become experts in bedside manner by reading a book or paper, you have to experience this knowledge or witness other people demonstrating it. Learning from example. For Collins and Evans, you only gain tacit knowledge by witnessing and spending time around other experts and observing (socialization, training, and experience)

· How much should we trust the different recommendations experts have and what are some problems associated with listening to experts?

· Only respecting/listening to certain kinds of experts leads to a narrowing of knowledge

· Theory of Lay Expertise . . . Many researchers argue that we don’t appreciate the range of experts that exist, we often dismiss people because they don’t have credentials/formal training, don’t “look like” traditional experts, or are perceived to be too close to situation

· EG. During AIDS, professional researchers were viewed as experts, but the different form of expertise of patients, family members, paramedics, etc. were ignored.

· Expertise may not be the same but it is complementary

· For too long, we thought the experts were the people wearing the labcoats; however, ‘lay communities’ have valuable expertise that is different from traditional expertise but still valuable.

· Experts can tell us how to do something, but they can’t tell us what to do.

· Theory: Honest Brokers (call them honest brokers because they provide you with guidance without trying to sway you to one side . . . first tell me what your goal is, then I will tell you the best way to achieve it. Honest brokers try to give you as many options as possible based on your goal, unlike issue advocates who give you one option and really push you to choose that ).

· When selecting an expert to rely on, you have to identify an expert who will be able to act as an honest broker and fulfill all those roles. When listening to experts on the news, you have to identify which role they assume because that will help you become a more astute citizen/listener)

· Knowledge Generation . . . what info/knowledge do we need and how are we going to acquire this info

· Knowledge Validation . . . once we have produced or acquired this knowledge, we have to decide whether it is trustworthy (ask questions regarding veracity, credibility, salience, legibility)

· Knowledge Circulation . . . After the knowledge has been deemed reliable, it needs to be shared with individuals who NEED the info

· Application . . . what do we do with this information, how do we apply it?

· James Scott . . . three components of “governmental regimes” or “bureaucratic logics”

· Simplification = removing elements that are seen as excessive or irrelevant or superfluous to what you are trying to achieve

· Legibility = simplifying things to increase legibility or make things easier to count/read/to fit into your databases or knowledge systems

· Manipulability = making it easier to manipulate your landscape

· By adhering to simplification, legibility, manipulability you unintentionally create a harmful monoculture

· We use S, L, and M in all four phases of Knowledge Systems because they help us achieve our knowledge systems goals.

· COMPSTAT and S, L, M . . . simplified crime into a two dimensional system, pressure to decrease crime turned things into a monoculture