Regular readers know I often write about identity, free will, and the narrative constraints of language. But I also explore these ideas through fiction, under the name Ridley Park.
In this short video, I unpack the philosophical motivations behind my stories, including:
Why reality is never as it seems
Why the self is a narrative convenience
What Heidegger’s Geworfenheit and Galen Strawson’s Causa Sui argument reveal about agency
And why language fails us – even when we think it serves
This isn’t promotional fluff. It’s epistemological dissent in a new format. Fictional, yes, but only in the sense that most of reality is, too.
A five-part descent into the illusion of autonomy, where biology writes the script, reason provides the excuse, and the self is merely the echo of its own conditioning.This is a follow-up to a recent post on the implausibility of free will.
Manipulability as Disproof
If your will can be altered without your consent, was it ever truly yours?
“If a button on the outside of your skull can change your morality, then where, exactly, is your autonomy hiding?”
Audio: NotebookLM podcast of this topic.
We’ve heard it all before:
“Sure, I’m influenced — but at the end of the day, I choose.” But what happens when that influence isn’t influence, but modulation? What if your very sense of right and wrong — your willingness to forgive, to punish, to empathise — can be dialled like a radio station?
And what if you never know it’s happening?
Your Morality Is Neurochemical
Studies using Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) have shown that moral judgments can be shifted by stimulating the dorsolateral prefrontal cortex (DLPFC).
Turn it up: the subject becomes more utilitarian.
Turn it down: the subject becomes more emotionally reactive.
They make different decisions in the exact same scenarios, depending on which neural pathway is dominant.
The kicker?
They always explain their choices as though they had made them deliberately.
There is no awareness of the manipulation. Only a retrospective illusion of authorship.
A|B Testing the Soul
Let’s run a thought experiment.
Scenario A: You’re well-fed, calm, unprovoked. Scenario B: You’re hungry, cortisol-spiked, primed with images of threat.
Same moral dilemma. Different choice.
Query both versions of you, and both will offer coherent post hoc justifications. Neither suspects that their “will” was merely a biochemical condition in drag.
If both versions feel authentic, then neither can claim authority.
Your will is not sovereign. It’s state-dependent. And if it changes without your knowledge, it was never really yours to begin with.
Even the Observer Is a Variable
To make matters worse: the person judging your decision is just as susceptible.
An irritated observer sees you as difficult. A relaxed one sees you as generous. The same action — different verdict.
And yet both observers think they are the neutral party. They are not. They are chemically calibrated hallucinations, mistaking their reaction for objective truth.
You’re a Vending Machine, Not a Virtuoso
This isn’t metaphor. It’s architecture.
You input a stimulus.
The brain processes it using pre-loaded scripts, shaped by hormones, past trauma, fatigue, blood sugar, social context.
An output emerges.
Then the brain rationalises it, like a PR firm cleaning up after a CEO’s impulse tweet.
Reason follows emotion. Emotion is involuntary. Therefore, your reasoning is not yours. It’s a post-event explanation for something you didn’t choose to feel.
TL;DR: If It Can Be Tweaked, It’s Not Yours
Your “moral core” can be adjusted without your awareness.
You justify manipulated choices with total confidence.
Your assessors are equally chemically biased.
There is no neutral version of “you” — just shifting states with internal coherence.
And if your choices depend on state, and your state can be altered, then freedom is a costume worn by contingency.
The Illusion of Clarity in a World of Cognitive Fog
Apologies in advance for this Logic 101 posting. Reason—our once-proud torch in the darkness, now more like a flickering lighter in a hurricane of hot takes and LinkedIn thought-leadership. The modern mind, bloated on TED Talks and half-digested Wikipedia articles, tosses around terms like “inductive” and “deductive” as if they’re interchangeable IKEA tools. So let us pause, sober up, and properly inspect these three venerable pillars of human inference: deduction, induction, and abduction—each noble, each flawed, each liable to betray you like a Greco-Roman tragedy.
Video: This post was prompted by this short by MiniPhilosophy.
Audio: NotebookLM podcast on this topic.
Deduction: The Tyrant of Certainty
Deduction is the purest of the lot, the high priest of logic. It begins with a general premise and guarantees a specific conclusion, as long as you don’t cock up the syllogism. Think Euclid in a toga, laying down axioms like gospel.
Example:
All humans are mortal.
Socrates is human.
Therefore, Socrates is mortal.
Perfect. Crisp. Unassailable. Unless, of course, your premise is bollocks. Deduction doesn’t check its ingredients—it just cooks with whatever it’s given. Garbage in, garbage out.
Strength: Valid conclusions from valid premises. Weakness: Blind to empirical falsity. You can deduce nonsense from nonsense and still be logically sound.
Induction: The Gambler’s Gospel
Induction is the philosopher’s lottery ticket: generalising from particulars. Every swan I’ve seen is white, ergo all swans must be white. Until, of course, Australia coughs up a black one and wrecks your little Enlightenment fantasy.
Example:
The sun rose today.
It rose yesterday.
It has risen every day I’ve been alive.
Therefore, the sun will rise tomorrow.
Touching, isn’t it? Unfortunately, induction doesn’t prove anything—it suggests probability. David Hume had an existential breakdown over this. Entire centuries of Western philosophy spiralled into metaphysical despair. And yet, we still rely on it to predict weather, markets, and whether that dodgy lasagna will give us food poisoning.
Strength: Empirically rich and adaptive. Weakness: One exception detonates the generalisation. Induction is only ever as good as the sample size and your luck.
Abduction: Sherlock Holmes’ Drug of Choice
Abduction is the inference to the best explanation. The intellectual equivalent of guessing what made the dog bark at midnight while half-drunk and barefoot in the garden.
Example:
The lawn is wet.
It probably rained.
It could be a garden sprinkler. Or a hose. Or divine intervention. But we bet on rain because it’s the simplest, most plausible explanation. Pragmatic, yes. But not immune to deception.
Strength: Useful in messy, real-world contexts. Weakness: Often rests on a subjective idea of “best,” which tends to mean “most convenient to my prejudices.”
The Modern Reasoning Crisis: Why We’re All Probably Wrong
Our contemporary landscape has added new layers of complexity to these already dubious tools. Social media algorithms function as induction machines on steroids, drawing connections between your click on a pasta recipe and your supposed interest in Italian real estate. Meanwhile, partisan echo chambers have perfected the art of deductive reasoning from absolutely bonkers premises.
Consider how we navigate information today:
We encounter a headline that confirms our worldview
We accept it without scrutiny (deductive failure)
We see similar headlines repeatedly (inductive trap)
We conclude our worldview is objectively correct (abductive collapse)
And thus, the modern reasoning loop is complete—a perfect system for being confidently incorrect while feeling intellectually superior.
Weakness by Analogy: The Reasoning Café
Imagine a café.
Deduction is the customer who checks the menu and confidently orders “Soup of the Day,” because the chalkboard says “Today’s Soup is Tomato,” and she trusts chalkboards.
Induction is the one who has had tomato soup every Wednesday for months and assumes it’ll be tomato today again—until it isn’t, and now he’s wearing bisque.
Abduction sees the waiter carrying bowls of red liquid to every table and infers it’s probably tomato soup, orders it, and gets… gazpacho. Ice-cold disappointment.
All three are trying to reason. Only one might get lunch.
The Meta-Problem: Reasoning About Reasoning
The true joke is this: we’re using these flawed reasoning tools to evaluate our reasoning tools. It’s like asking a drunk person to judge their own sobriety test. The very mechanisms we use to detect faulty reasoning are themselves subject to the same faults.
This explains why debates about critical thinking skills typically devolve into demonstrations of their absence. We’re all standing on intellectual quicksand while insisting we’ve found solid ground.
Conclusion: Reason Is Not a Guarantee, It’s a Wager
None of these modalities offer omniscience. Deduction only shines when your axioms aren’t ridiculous. Induction is forever haunted by Hume’s skepticism and the next black swan. Abduction is basically educated guessing dressed up in tweed.
Yet we must reason. We must argue. We must infer—despite the metaphysical vertigo.
The tragedy isn’t that these methods fail. The tragedy is when people believe they don’t.
Perhaps the wisest reasoners are those who understand the limitations of their cognitive tools, who approach conclusions with both confidence and humility. Who recognize that even our most cherished beliefs are, at best, sophisticated approximations of a reality we can never fully grasp.
So reason on, fellow thinkers. Just don’t be too smug about it.
I’ve gone entirely off the reservation (send help, or biscuits) and decided, in a fit of masochistic curiosity, to crack open Measure What Mattersby John Doerr—a business management tome that’s been gathering dust on my shelf longer than most CEOs last in post.
Spoiler Alert: I won’t be finishing it. Nor, frankly, should you.
Full disclosure before we all get the wrong idea: I find self-help books about as nourishing as a rice cake made of existential despair. Add “business” or “management” into the mix, and you’re cooking up something so vapid it could qualify as a greenhouse gas.
Audio: NotebookLM podcast of this content.
Measure What Matters reads less like a serious work of business philosophy and more like a self-important infomercial, peddling the sort of common sense you could overhear in a pub toilet after three pints. And, like any decent infomercial, it’s drenched in “inspirational” stories so grandiose you’d think Moses himself was consulting for Google.
Image: Midjourney’s rendering of a possible cover image. Despite the bell protruding from the crier’s head, I went with a ChatGPT Dall-E render instead.
I’m sure Doerr genuinely believes he’s handing down managerial tablets from Mount Sinai, and I’m equally sure he’s eating his own dog food with a knife and fork. But what gets served up here is a steaming dish of selection bias, smothered with a rich gravy of hand-waving nonsense.
What am I getting my knickers in a twist about? What’s this book actually about?
In short: three letters—OKR. That’s Objectives and Key Results, for those of you not fluent in MBA-speak. These mystical artefacts, these sacred runes, are supposed to propel your company from the gutter to the stars. Intel did it. Google did it. Ergo, you too can join the pantheon of tech demi-gods. (Provided, of course, you were already a billion-dollar operation before you started.)
Nobody’s going to argue that having goals is a bad idea. Nobody’s throwing the baby out with the Gantt chart. But goals are nebulous, wishy-washy things. “I want to travel” is a goal. “I will cycle and kayak my way to Edinburgh by the end of the year, preferably without dying in a ditch”—that’s an objective.
Businesses, being the lumbering beasts they are, naturally have goals. Goals for products, customers, market share, quarterly bonuses, and ritualistic victory dances in front of their crushed competitors. Nothing new there.
According to Doerr and the gospel of OKRs, however, the only thing standing between you and unassailable market dominance is the right set of buzzwords stapled to your quarterly reports. Apparently, Intel crushed Motorola not because of innovation, talent, or dumb luck—but because they set better OKRs. (History books, please update yourselves accordingly.)
Video: John Doerr’s 2018 TED Talk on this topic.
But wait, what’s an OKR again? Ah yes: we’ve done Objectives. Now for the Key Results bit. Basically, you slap some numbers on your wish list. If you’ve survived in business longer than a fruit fly, you’ve already met KPIs (Key Performance Indicators)—another Three Letter Acronym, because we live and die by alphabet soup. Key Results are KPIs wearing slightly trendier trainers.
Example: “We will be number one by the third quarter by prospecting a dozen companies and closing three deals by September.” Marvellous. Life-changing. Nobel-worthy. Now go forth and conquer.
Right. Now that I’ve saved you twenty quid and several hours of your life, let’s talk about why this book is still an exercise in masturbatory futility.
First, and most fatally, it’s predicated on selection bias so profound it should come with a health warning. Allow me to paint you a picture. Imagine we’re advising a football league. Every team sets OKRs: target weights, goal tallies, tackles, penalty avoidance—the works. Everyone’s focused. Everyone’s motivated. Everyone’s measuring What Matters™.
Come the end of the season, who wins? One team. Did they win because their OKRs were shinier? Because they ‘wanted it more’? Or, just maybe, did they win because competition is brutal, random, and often unfair?
This is the problem with false meritocracies and the illusion of control. It’s like thanking God for your touchdown while assuming the other team were all godless heathens who deserved to lose. It’s the same nonsense, in a suit and tie.
Will our winning team win next year? Doubtful. Did Intel lose ground later because they forgot how to spell OKR? No. Because the world changes, markets collapse, and sometimes you’re just standing on the wrong bit of deck when the ship goes down.
Then there’s the love affair with plans. In theory, lovely. In practice, arbitrary. You can set as many Objectives as you like, but what counts as a “win”? Is it profit? Market share? Not dying of ennui?
The free market worshippers among us love to preach that governments can’t plan effectively, unlike the rugged gladiators of capitalism. Funny how businesses, in their infinite wisdom, are then urged to behave like microcosmic Soviet Five-Year Planners, drowning in metrics and objectives. Topically, we are living through the charming consequences of governments trying to run themselves like corporations—newsflash: it’s not going splendidly.
In short: companies are not nations, and OKRs are not magic bullets.
What else is wrong with this book? Well, to start: it’s shallow. It’s smug. It peddles survivorship bias with the zeal of a televangelist. It confuses correlation with causation like an over-eager undergraduate. And most damning of all, it sells you the fantasy that success is just a matter of writing smarter lists, as if strategy, luck, market forces, and human frailty were irrelevant footnotes.
Measure What Matters doesn’t measure anything except the reader’s patience—and mine ran out somewhere around chapter five.
Greed is a vice we readily recognise in others but often overlook in ourselves. This selective perception was strikingly evident during a recent conversation I had with a man who was quick to condemn another’s greed while remaining oblivious to his own similar tendencies. I told him about the escalating greed of certain companies who profit greatly from selling their printer inks and toner brands. I’ll spare you this history. This encounter underscores the powerful influence of fundamental attribution bias on our judgments and self-awareness.
Exploring Greed
Greed can be defined as an intense and selfish desire for something, especially wealth, power, or food. Psychologically, it is considered a natural human impulse that, when unchecked, can lead to unethical behaviour and strained relationships. Societally, greed is often condemned, yet it persists across cultures and histories.
We tend to label others as greedy when their actions negatively impact us or violate social norms. However, when we aggressively pursue our interests, we might frame it as ambition or resourcefulness. This dichotomy reveals a discrepancy in how we perceive greed in ourselves versus others.
Understanding Fundamental Attribution Bias
Fundamental attribution bias, or fundamental attribution error, is the tendency to attribute others’ actions to their character while attributing our own actions to external circumstances. This cognitive bias allows us to excuse our behaviour while holding others fully accountable for theirs.
For example, if someone cuts us off in traffic, we might think they’re reckless or inconsiderate. But if we cut someone off, we might justify it by claiming we were late or didn’t see them. This bias preserves our self-image but distorts our understanding of others.
The Conversation
Our conversation was centred on an HP printer that has shown a ‘low ink – please replace’ message since the cartridge was first installed. I recounted the history of the ink and toner industry. HP had a monopoly on ink for their products, a situation that earned them substantial marginal profits. Upstarts entered the marketplace. This started an escalating arms war. HP spent R&D dollars trying to defend their profit margins with nil benefit to the consumers of their product. In fact, it kept costs artificially higher. Competitors who wanted a slice of those fat margins found ways around these interventions. Eventually, HP installed chips on their toner cartridges. Unfortunately, they have a bug – or is it a feature? If you install a cartridge and remove it, it assumes you’re up to something shady, so it spawns this false alert. Some people believe this out of hand, so HP benefits twice.
If this bloke had worked for HP and had been responsible for revenue acquisition and protection, he would have swooned over the opportunity. Have no doubt. At arm’s length, he recognised this as sleazy, unethical business practices.
This conversation revealed how easily we can fall into the trap of judging others without reflecting on our own behaviour. His indignation seemed justified to him, yet he remained unaware of how his actions mirrored those he criticised.
Biblical Reference and Moral Implications
This situation brings to mind the biblical passage from Matthew 7:3-5:
“Why do you look at the speck of sawdust in your brother’s eye and pay no attention to the plank in your own eye? … You hypocrite, first take the plank out of your own eye, and then you will see clearly to remove the speck from your brother’s eye.”
The verse poignantly captures the human tendency to overlook our flaws while magnifying those of others. It calls for introspection and humility, urging us to address our shortcomings before passing judgment.
The Asymmetry of Self-Perception
Several psychological factors contribute to this asymmetry:
Self-Serving Bias: We attribute our successes to internal factors and our failures to external ones.
Cognitive Dissonance: Conflicting beliefs about ourselves and our actions create discomfort, leading us to rationalize or ignore discrepancies.
Social Comparison: We often compare ourselves favourably against others to boost self-esteem.
This skewed self-perception can hinder personal growth and damage relationships, as it prevents honest self-assessment and accountability.
Overcoming the Bias
Awareness is the first step toward mitigating fundamental attribution bias. Here are some strategies:
Mindful Reflection: Regularly assess your actions and motivations. Ask yourself if you’re holding others to a standard you’re not meeting. Riffing from ancient moral dictates, just ask yourself if this is how you would want to be treated. Adopt Kant’s moral imperative framework.
Seek Feedback: Encourage honest input from trusted friends or colleagues about your behaviour.
Empathy Development: Practice seeing situations from others’ perspectives to understand their actions more fully.
Challenge Assumptions: Before making judgments, consider external factors that might influence someone’s behaviour.
By actively recognising and adjusting for our biases, we can develop more balanced perceptions of ourselves and others.
Conclusion
The irony of condemning in others what we excuse in ourselves is a common human pitfall rooted in fundamental attribution bias. The adage, ‘Know thyself’ might come into view here. We can overcome these biases by striving for self-awareness and empathy, leading to more authentic relationships and personal integrity.
The Cognitive Process Flow Model illustrates how we process the phenomenal world. It’s reductionist and is missing aspects because it is just a back-of-the-napkin sketch. I created it because I uttered, “I can model it for you”. And so I did.
EDIT: I’ve updated the model slightly as the article head image, but the copy content refers to the first draft.
My response was to a person making the claim, that all you need to facts and logic prevails. Rather than restate the argument, I’ll just walk through the diagramme.
There’s meta information to set it up. We are subjective entities in the world. We have a sense-perception apparatus as we exist in it. Countless events occur in this world. We recognise only a few of them within our limited range, though technology expands this range in various ways.
Most of us interact in the world. Some are less ambulatory, so the world visits them. Some have sense-perception deficits whilst others have cognitive deficits. My point is not to capture every edge and corner case. This is just a generalised model.
All models are wrong, but some are useful.
—George Box
It starts with an event. Events occur ceaselessly. In our small portion of the world and elsewhere. For the purpose of the model, the first thing that happens is an event catches our attention. We might notice a shape, a colour, or a movement; we might hear a sound, smell an aroma, feel a sensation, or taste something.
A pre-emotion, pre-logic function serves to process these available inputs. Perhaps, you hear a report on anthropogenic climate change or read something about a political candidate. This emotional filter will police sensory inputs and unconsciously or preconsciously determine if you will react to the initial stimulus. If not, you’ll continue in an attention-seeking loop. Not that kind of attention-seeking.
As my dialogue was about the presentation of facts, our next stop will be logical evaluation. Does this make sense to us, or can we otherwise make it? This is a process in itself. I’ll assume here that it requires little elaboration. Instead, I’ll focus on the operating environment.
Our logical processes are coloured by past experiences and tainted by cognitive biases and deficits. We may also trigger the calling of additional facts through past experiences or the current engagement.
We’ll process these fragments and reach some logical conclusion. But we’re not done. We take this intermediate conclusion and run it through more emotional processing. Cognitive biases come back into play. If the event conforms with your past experiences and cognitive biases, we may run it through a cognitive dissonance routine. To be honest, this probably is part of the emotional reconciliation process, but I’ve drawn it here, so I’ll let it be. In this case, it’s just a filter. If it happens to conform to our belief system, it will pass unfettered; otherwise, it will be squared with our beliefs. Again, this leads me to believe it’s a subcomponent of emotional reconciliation. I’ll update the chart later.
In any case, we’ll end at Final Acceptance. This acceptance may be that we accept or reject the logic, but we arrive at an opinion that gets catalogued with the rest of them. Some may be elevated to facts or truths in the epistemological hierarchy. Although an end marker is identified, it’s really a wait state for the next event. Rinse and repeat until death.
I’ll update this presently. Be on the lookout. It could include more dimensions and interactions, but that might have to wait until version 3.
Meantime, does this feel right to you? Did it even get your attention?
An Example: Anthropogenic Climate Change
Let’s wrap up with an example. I’ll use climate change. An article comes into your attention field, and you have an interest in these things, so it passes through the emotional filter. If your propensity for these articles is high, it might race to the next stage.
You read the article, and it contains some facts—rather, it contains claims for evaluation. To do this, you’ll recall past experiences and cognitive biases are always lying in wait. You may have to look for new facts to add to the mix. These will have to take a similar route past your attention gatekeeper and emotional sidekick.
If you are already predisposed that climate change is a hoax, these facts will filter through that lens—or vice versa.
When all of this is resolved, you’ll have arrived at a conclusion—perhaps we’ll call it a proto-conclusion. It hasn’t been set yet.
You are still going to introspect emotionally and decide if this is a position you want to hold. Perhaps, you feel that climate change is a hoax but this doesn’t jive with that position. Here, you’ll either accept these facts and flip a bit to a sceptical believer or cognitive dissonance will kick in and ensure your sense of the world isn’t thrown off kilter. You may update your belief system to include this datum for future assessments.
Now we are ready for final acceptance. You can now express your established opinion. If the net event is to counter that acceptance, rinse and repeat ad infinitum.
As I make progress on my Democracy: The Great Illusion book, I am working on a chapter considering juries as a microcosm of a democratic process. I am debating whether to include this information in the chapter, Like the justice system in the United States, perhaps I should just flip a coin.
Having been seated on three juries in my lifetime, it was just as I had expected. Thankfully, I’ve never been on the other side of this Russian Roulette revolver. We really need to limit human-based decision systems. I have as much faith in juries as I have with democracies, but I thought I might take a peek into deliberation at a smaller scale, perhaps 12 jurors.
I read Kahneman’s Noise: A Flaw in Human Judgment when it was published in 2021, and I was not at all surprised how trial outcomes, quite often led to markedly different outcomes depending on the judge and the defendant. Although discrepancies based on sex and race have been well documented, this book showcased how much the judge’s mood affected sentencing—particularly if s/he was hungry. But this was not my interest.
My interest was to assess how the same case might fare given different judges and juries, so I asked ChatGPT. I want to share what it had to say—edited slightly for readability and flow.
ChatGPT Transcript
There have been several studies and controlled experiments assessing the variability in trial outcomes when the same case is tried separately by different judges and juries. These studies aim to evaluate the consistency and reliability of judicial decision-making, shedding light on the fallibility and variability of trial outcomes. Here are some key findings from such research:
Studies on Judicial and Jury Variability
The American Jury Study
One of the most notable studies is “The American Jury” by Harry Kalven and Hans Zeisel. This extensive study compared the decisions of juries and judges in actual cases. It found that juries and judges often agree on verdicts, but there are significant instances where they disagree. This disagreement highlights the subjective nature of decision-making and the potential for variability in trial outcomes.
Judge-Jury Agreement
Research by Valerie Hans and Neil Vidmar in “Judging the Jury” indicates that judges and juries reach the same verdict about 75-80% of the time. However, in the remaining 20-25% of cases, the disagreement can lead to different outcomes for the same case. These findings suggest that personal interpretations and biases can influence verdicts.
judges and juries reach the same verdict about 75-80% of the time
The Civil Jury Project
The Civil Jury Project at NYU School of Law has conducted several studies examining jury decision-making. Their research has highlighted how factors like juror demographics, pre-existing biases, and the presentation of evidence can lead to different outcomes in similar cases.
Controlled Experiments
Controlled experiments, such as those conducted by Shari Seidman Diamond and her colleagues, have involved mock trials where participants (acting as jurors) are presented with the same case. These experiments have shown significant variability in verdicts and sentences, demonstrating the influence of group dynamics, individual biases, and the presentation of evidence on jury decisions.
Key Findings
Cognitive Bias and Heuristics
Studies indicate that cognitive biases and heuristics significantly impact both judges and juries. Biases such as the anchoring effect, where the initial piece of evidence presented influences subsequent judgments, can lead to different outcomes for the same case.
Case Complexity and Juror Understanding
Research has shown that the complexity of legal cases can lead to variability in outcomes. Jurors often struggle with understanding complex legal and technical issues, leading to decisions based on incomplete or misunderstood information.
Influence of Legal Instructions
The clarity and comprehensibility of legal instructions given to juries play a crucial role in decision-making. Studies have found that poorly understood instructions can result in inconsistent application of the law, leading to variable outcomes.
Implications
The variability in trial outcomes highlights the fallibility of judicial processes and underscores the importance of reforms to enhance consistency and fairness. Potential reforms could include:
Improving Juror Education: Providing better education and resources for jurors to understand legal principles and case-specific details.
Reducing Cognitive Bias: Implementing measures to mitigate cognitive biases, such as standardized jury instructions and blind procedures.
Enhancing Legal Instructions: Ensuring that legal instructions are clear, concise, and comprehensible to prevent misinterpretation.
Utilizing Expert Testimony: Increasing the use of expert testimony to clarify complex issues for jurors.
Conclusion
The variability in trial outcomes, when the same case is tried by different judges and juries, underscores the need for ongoing research and reform in judicial processes. By understanding the factors that contribute to inconsistent verdicts, we can develop strategies to enhance the fairness and reliability of the justice system.
References
Kalven, H., & Zeisel, H. (1966). The American Jury. Little, Brown and Company.
Hans, V., & Vidmar, N. (1986). Judging the Jury. Plenum Press.
Diamond, S. S., & Rose, M. R. (2005). Real Juries: The Power of Watching. Basic Books.
The Civil Jury Project at NYU School of Law. (n.d.). Research on Jury Decision Making. Retrieved from [website].
Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. Little, Brown Spark.
Using AutoCrit, I continue to share the review progress of my work in progress, Democracy: The Grand Illusion—perhaps Grand Delusion might be more fitting. In this chapter, I establish a foundation for intelligence and cognitive function.
Synopsis
The text begins by discussing the concept of intelligence, specifically focusing on IQ as a measure of cognitive abilities relative to others. It explains the origins and standardisation of IQ tests, highlighting their limitations in capturing the full spectrum of human intelligence. The discussion then shifts towards Emotional Intelligence (EQ), outlining its components and emphasising its importance in interpersonal relationships and leadership roles.
The narrative further delves into Howard Gardner’s Theory of Multiple Intelligences, which challenges the idea that intelligence is a singular ability measured solely by traditional IQ tests. The text elaborates on various types of intelligences proposed by Gardner, such as linguistic, musical, spatial, naturalistic, and intrapersonal intelligence.
Moreover, cognitive biases are explored in detail within democratic processes through real-world examples like confirmation bias or groupthink. Strategies to mitigate these biases are suggested for improving decision-making within democracies.
The text concludes with a call for embracing diverse forms of intelligence within democratic systems while acknowledging and addressing cognitive biases to enhance governance effectiveness.
Audience
The target audience for this text appears to be individuals interested in psychology, education theory, and political science, or those exploring the intersection between human cognition and democratic governance. Readers seeking an in-depth analysis of different forms of intelligence alongside discussions on democracy would find this text engaging.
Those less inclined towards academic or theoretical discourse may not be the primary target audience. To make it more relevant to a broader readership base outside academia or specialized fields:
Simplifying complex terminology
Providing relatable examples
Incorporating practical applications
Structure and Organisation
The structure follows a logical order starting with defining traditional measures of intelligence leading up to discussions on multiple intelligences and cognitive biases impacting democracy. Each section flows cohesively into the next without abrupt transitions or disjointed topics. No significant issues with organisation are evident; each subsection builds upon previous concepts effectively.
Clarity
Overall clarity is maintained throughout most sections; however:
Complex sentence structures could potentially hinder comprehension for some readers.
Jargon related to psychological theories might require additional clarification for lay audiences. Providing simplified explanations where needed can enhance reader understanding without sacrificing depth.
Argument and Persuasion
Opinions presented focus more on informing than persuading; strengths lie in presenting well-supported arguments backed by historical context (e.g., case studies). Logical construction aids credibility but lacks explicit attempts at persuasion beyond informative purposes.
Tone
The tone remains informative yet engaging throughout without veering towards overly formal or casual language usage which suits an academic discourse effectively.
Interest and Engagement
The text manages to maintain interest through its exploration of diverse aspects related to human intelligence; however certain sections discussing specific types like “Naturalistic Intelligence” might require additional engagement strategies such as case studies showcasing individuals excelling in that particular domain.
Final Thoughts & Conclusions
The conclusions drawn at the end tie together various points introduced earlier effectively providing clear insights into how embracing diverse forms of intelligence can enhance democratic decision-making processes – offering strong closure that resonates with preceding discussions.
The text concludes by emphasising the importance of understanding intelligence in a multifaceted manner, encompassing both IQ and EQ as well as Howard Gardner’s theory of multiple intelligences. It highlights the limitations of relying solely on IQ tests for measuring intelligence, pointing out cultural biases and the narrow scope of such assessments. The discussion on emotional intelligence (EQ) adds depth to the exploration, underscoring its significance in interpersonal relationships, leadership, and mental health. By integrating multiple intelligences into the context of democracy, the text suggests a more inclusive approach to decision-making that values diverse forms of intelligence beyond traditional analytical skills. Overall, the conclusion effectively ties together key points introduced throughout the text, providing a comprehensive perspective on human intelligence and its implications for democratic systems.
Binet, A., & Simon, T. (1916). The development of intelligence in children. (E. S. Kite, Trans.). Williams & Wilkins.
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I mentioned in my last post about how Artificial Intelligence discovered a new variable—or, as the claim suggests, a new physics. This was a tie-in to the possible missing dimensions of human perception models.
Without delving too deep, the idea is that we can predict activity within dynamic systems. For example, we are all likely at least familiar with Newtonian physics—postulates such as F = ma [Force equals mass times acceleration or d = vt [distance equals velocity times time] and so on. In these cases, there are three variables that appear to capture everything we need to predict one thing given the other two that need to remain constant. Of course, we’d need to employ calculus instead of algebra if these are not constant. A dynamic system may require linear algebra instead.
When scientists represent the world, they tend to use maths. As such, they need to associate variables as proxies for physical properties and interactions in the world. Prominent statistician, George Box reminds us that all models are wrong, but some are useful. He repeated this sentiment many times, instructing us to ‘remember that models are wrong: the practical question is how wrong do they have to be to not be useful‘. But no matter how hard we try, a model will never be the real thing. The map cannot become the terrain, no matter how much we might expect it to be. By definition, a model is always an approximation.
All models are wrong but some are useful
George Box
In the Material Idealism post, the embedded video featuring Bernardo Kastrup equated human perception to the instrumentation panels of an aeroplane. Like the purported observer in a brain, the pilot can view the instruments and perform all matters of actions to manipulate the plane, including taking off, navigating through the environment, avoiding obstacles, and then landing. But this instrumentation provides only a representation of what’s ‘really’ outside.
Like mechanisms in the body, instrumentation can be ‘wired’ to trigger all sorts of warnings and alerts, whether breached thresholds or predictions. The brain serves the function of a predictive difference engine. It’s a veritable Bayesian inference calculator. Anil Seth provides an accessible summary in Being You. It relies on the senses to deliver input. Without these sense organs, the brain would be otherwise unaware and blinded from external goings on.
The brain cannot see or hear. It interprets inputs from eyes and ears to do so. Eyes capture light-oriented events, which are transmitted to the brain via optic nerves, and brain functions interpret this information into colour and shape, polarisation and hue, depth and distance, and so on. It also differentiates these data into friend or foe signals, relative beauty, approximate texture, and such. Ears provide a similar function within their scope of perception.
As mentioned, some animals have different sense perception capabilities and limitations, but none of these captures data not also accessible to humans via external mechanisms.
Some humans experience synesthesia, where they interpret certain stimuli differently, perhaps hearing colours or smelling music. We tend to presume that they are the odd ones out, but this assumption does not make it so. Perhaps these people are actually ahead of the rest of us on an evolutionary scale. I suppose time might sort that one out.
But here’s the point. Like the pilot, we can only experience what we are instrumented to experience, as limited to our sense perception and cognition faculties. If there are events not instrumented, it will be as if they don’t exist to the pilot. Can the pilot hear what’s happening outside?
This is the point of the AI experiment referenced above. Humans modelled some dynamic process that was presumed to be ‘good enough’, with the difference written off as an error factor. Artificial Intelligence, not limited to human cognitive biases, found another variable to significantly reduce the error factor.
According to the theory of evolution, humans are fitness machines. Adapt or perish. This is over-indexed on hereditary transmission and reproduction, but we are more vigilant for things that may make us thrive or perish versus aspects irrelevant to survival. Of course, some of these may be benign and ignored now but become maleficent in future. Others may not yet exist in our realm.
In either case, we can’t experience what we can’t perceive. And as Kastrup notes, some things not only evade perception but cannot even be conceived of.
I am not any more privileged than the next person to what these missing factors are nor the ramifications, but I tend to agree that there may be unknown unknowns forever unknowable. I just can’t conceive what and where.
How Soon Is Now? Is there anything beyond now—is there a future? Was there a past? What better occasion to reflect on this than the turn of a new year, of a new decade?
Now is easy. It right here, and here, and here, and here… and interminable series of heres. The past is easy, too, we were there—the accumulation of former heres—, so at least we can claim it was real at the time—or as real as we could perceive and can imagine. Memory frailties notwithstanding, the past is indelible. Whether we are or can be aware is another story.
Past is different to history. Past is an event or events. We may not even become aware of these events until they have passed—perhaps centuries or millennia later. These may be historicised. History is a story. In French, the terms aren’t even separated. L’histoire is simultaneously a story and history, a reminder of how inextricable they are.
But what about the future? A conceptual future is a fairly new human construct. Some events occuring after now have happened since the beginning of time. In fact without time or the invention of a notion of time, there can be no future or past. It’s been said that time is what keeps everything from happening at once.
“Time is what keeps everything from happening at once.”
We talk about the future, but when we reference it now, it’s only some speculative future—some admixture of uncertainty and probabilities. There are no guarantees any given event will actually manifest, whether we will be there to experience it, or whether any future will even arrive. This is a known limitation of empiricism. That the sun has risen for some 4 billion years doesn’t guarantee it will rise tomorrow. There is nothing necessarily preventing the universe from ceasing to exist tomorrow or in an instant, pardoning the nomenclature of time.
Where our perception of now is already quite limited in scope and experience, any notion of future is decidedly worse. And of all of the possible threads and imagined threads, only one will manifest—unless you subscribe to parallel universe models, in which case you can still only experience one and only one, at least for the time being.
From the perspective of now, the future, like history, is just a story. In these times of COVID, we should realise that some stories hadn’t been written. Similar storylines had been imagined and authored, but the one that manifest was different still. Truth is stranger than fiction—and worse.
But does the future exist? Can we discuss the future other than conceptually? Is the notion of future reserved for a privileged few? One so-called cognitive bias is that humans favour now and near-term events over further future events? From an evolutionary perspective, this makes perfect sense. First, I am here now, and whether I am here to experience the future remains to be seen. This bias is the basis for why most people don’t save enough for a comfortable retirement—a retirement with a comparable standard of living and quality of life as one’s ‘productive’ years.
One consideration is expected lifespan. Actuarially, a person might be expected to live on average, say, 76 years. If people my family historically live to 65 and I expect to retire thereabouts, saving past that is inefficient—transferring wealth across generations notwithstanding. If I die at 65, there is no mismatch. If I die at 76, then oopsie. Retirement income and savings is predominantly a First-world problem—a challenge for people who live in an income-based, consumerist society, so worrying about the future takes on a more relevance.
Even if I expect my village, tribe, or family care for me in my twilight years, there is still a notion of future to consider. Will they be there for me. But from an evolutionary perspective, this doesn’t necessitate a future beyond a generation, so the probability of an uncertain event is lower than, say, a thousand years from now.
NB: What had been a concept riffing on Hoffman’s evolutionary argument against reality was intercepted by the related notion of the future. I hope to return to Hoffman presently—if the future allows.
Philosophics 