1.This kids coloring book drawing that accidentally makes this woman look...curvier: · 2.This hand sanitizer bottle that's bound to cause a very thirsty person to make a big mistake:

48 Dumb, Misleading, Or Inappropriate Designs That Businesses Clearly Did Not Think Through At All

Clearly

clearly

How to Write Clearly: Rules and Exercises on English Composition by Edwin Abbott Abbott, is part of the HackerNoon Books Series. You can jump to any chapter in this book . PREFACE. here PREFACE. Almost every English boy can be taught to write clearly, so far at least as clearness depends upon the arrangement of words. Force, elegance, and variety of style are more difficult to teach, and far more difficult to learn; but clear writing can be reduced to rules. To teach the art of writing clearly is the main object of these Rules and Exercises. Ambiguity may arise, not only from bad arrangement, but also from other causes—from the misuse of single words, and from confused thought. These causes are not removable by definite rules, and therefore, though not neglected, are not prominently considered in this book. My object rather is to point out some few continually recurring causes of ambiguity, and to suggest definite remedies in each case. Speeches in Parliament, newspaper narratives and articles, and, above all, resolutions at public meetings, furnish abundant instances of obscurity arising from the monotonous neglect of some dozen simple rules. The art of writing forcibly is, of course, a valuable acquisition—almost as valuable as the art of writing clearly. But forcible expression is not, like clear expression, a mere question of mechanism and of the manipulation of words; it is a much higher power, and implies much more. Writing clearly does not imply thinking clearly. A man may think and reason as obscurely as Dogberry himself, but he may (though it is not probable that he will) be able to write clearly for all that. Writing clearly—so far as arrangement of words is concerned—is a mere matter of adverbs, conjunctions, prepositions, and auxiliary verbs, placed and repeated according to definite rules.[1] Even obscure or illogical thought can be clearly expressed; indeed, the transparent medium of clear writing is not least beneficial when it reveals the illogical nature of the meaning beneath it. On the other hand, if a man is to write forcibly, he must (to use a well-known illustration) describe Jerusalem as "sown with salt," not as "captured," and the Jews not as being "subdued" but as "almost exterminated" by Titus. But what does this imply? It implies knowledge, and very often a great deal of knowledge, and it implies also a vivid imagination. The writer must have eyes to see the vivid side of everything, as well as words to describe what he sees. Hence forcible writing, and of course tasteful writing also, is far less a matter of rules than is clear writing; and hence, though forcible writing is exemplified in the exercises, clear writing occupies most of the space devoted to the rules. Boys who are studying Latin and Greek stand in especial need of help to enable them to write a long English sentence clearly. The periods of Thucydides and Cicero are not easily rendered into our idiom without some knowledge of the links that connect an English sentence. There is scarcely any better training, rhetorical as well as logical, than the task of construing Thucydides into genuine English; but the flat, vague, long-winded Greek-English and Latin-English imposture that is often tolerated in our examinations and is allowed to pass current for genuine English, diminishes instead of increasing the power that our pupils should possess over their native language. By getting marks at school and college for construing good Greek and Latin into bad English, our pupils systematically unlearn what they may have been allowed to pick up from Milton and from Shakespeare. I must acknowledge very large obligations to Professor Bain's treatise on "English Composition and Rhetoric," and also to his English Grammar. I have not always been able to agree with Professor Bain as to matters of taste; but I find it difficult to express my admiration for the systematic thoroughness and suggestiveness of his book on Composition. In particular, Professor Bain's rule on the use of "that" and "which" (see Rule 8) deserves to be better known.[2] The ambiguity produced by the confusion between these two forms of the Relative is not a mere fiction of pedants; it is practically serious. Take, for instance, the following sentence, which appeared lately in one of our ablest weekly periodicals: "There are a good many Radical members in the House cannot forgive the Prime Minister for being a Christian." Twenty years hence, who is to say whether the meaning is " , i.e. members in the House," or "there are a good many Radical members of the House cannot &c."? Professor Bain, apparently admitting no exceptions to his useful rule, amends many sentences in a manner that seems to me intolerably harsh. Therefore, while laying due stress on the utility of the rule, I have endeavoured to point out and explain the exceptions. who and they all the Radical that The rules are stated as briefly as possible, and are intended not so much for use by themselves as for reference while the pupil is working at the exercises. Consequently, there is no attempt to prove the rules by accumulations of examples. The few examples that are given, are given not to prove, but to illustrate the rules. The exercises are intended to be written out and revised, as exercises usually are; but they may also be used for instruction. The books being shut, the pupils, with their written exercises before them, may be questioned as to the reasons for the several alterations they have made. Experienced teachers will not require any explanation of the arrangement or rather non-arrangement of the exercises. They have been purposely mixed together unclassified to prevent the pupil from relying upon anything but his own common sense and industry, to show him what is the fault in each case, and how it is to be amended. Besides references to the rules, notes are attached to each sentence, so that the exercises ought not to present any difficulty to a painstaking boy of twelve or thirteen, provided he has first been fairly trained in English grammar. vivâ voce The "Continuous Extracts" present rather more difficulty, and are intended for boys somewhat older than those for whom the Exercises are intended. The attempt to modernize, and clarify, so to speak, the style of Burnet, Clarendon, and Bishop Butler,[3] may appear ambitious, and perhaps requires some explanation. My object has, of course, not been to the style of these authors, but to show how their meaning might be expressed more clearly in modern English. The charm of the style is necessarily lost, but if the loss is recognized both by teacher and pupil, there is nothing, in my opinion, to counterbalance the obvious utility of such exercises. Professor Bain speaks to the same effect:[4] "For an English exercise, the matter should in some way or other be supplied, and the pupil disciplined in giving it expression. I know of no better method than to prescribe passages containing good matter, but in some respects imperfectly worded, to be amended according to the laws and the proprieties of style. Our older writers might be extensively, though not exclusively, drawn upon for this purpose." improve upon To some of the friends whose help has been already acknowledged in "English Lessons for English People," I am indebted for further help in revising these pages. I desire to express especial obligations to the Rev. J. H. Lupton, late Fellow of St. John's College, Cambridge, and Second Master of St. Paul's School, for copious and valuable suggestions; also to several of my colleagues at the City of London School, among whom I must mention in particular the Rev. A. R. Vardy, Fellow of Trinity College, Cambridge. * * * * * Before electrotyping the Fourth and Revised Edition, I wish to say one word as to the manner in which this book has been used by my highest class, as a collection of Rules for reference in their construing lessons. In construing, from Thucydides especially, I have found Rules 5, 30, 34, 36, 37, and 40_a_, of great use. The rules about Metaphor and Climax have also been useful in correcting faults of taste in their Latin and Greek compositions. I have hopes that, used in this way, this little book may be of service to the highest as well as to the middle classes of our schools. FOOTNOTES: [1] Punctuation is fully discussed in most English Grammars, and is therefore referred to in this book only so far as is necessary to point out the slovenly fault of trusting too much to punctuation, and too little to arrangement. [2] Before meeting with Professor Bain's rule, I had shown that the difference between the Relatives is generally observed the Shakespeare. See "Shakespearian Grammar," paragraph 259. [3] Sir Archibald Alison stands on a very different footing. The extracts from this author are intended to exhibit the dangers of verbosity and exaggeration. [4] "English Composition and Rhetoric," p. vii. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edwin Abbott Abbott (2007). How to Write Clearly: Rules and Exercises on English Composition. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/22600/pg22600.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Almost every English boy can be taught to write clearly

How to Write Clearly: Rules and Exercises on English Composition by Edwin Abbott Abbott is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : How to Write Clearly: Rules and Exercises on English Composition Title : Edwin Abbott Abbott Author : September 14, 2007 [eBook #22600] Most recently updated: January 2, 2021 Release date : English Language TABLE OF LINKS PREFACE INDEX OF RULES. I. CLEARNESS AND FORCE. . WORDS ORDER OF WORDS IN A SENTENCE. II. BREVITY. CLEARNESS AND FORCE. WORDS. ORDER OF WORDS IN A SENTENCE. BREVITY. EXERCISES CONTINUOUS EXERCISES CLEARNESS. BURNET. BISHOP BUTLER. BREVITY. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edwin Abbott Abbott (2007). How to Write Clearly: Rules and Exercises on English Composition. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/22600/pg22600.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

How to Write Clearly: Rules and Exercises on English Composition - Table of Links

Dependency injection (DI) is a wonderful thing. Simply add your dependency as a parameter to the constructor (most commonly) of your class, register it with you DI container, and away you go - the DI container will manage the rest. Some of the key benefits of DI are: greater testability, greater maintainability, and greater reusability. : { { ( dbContent = MyDbContext())
        {
            dbContext.Orders.Add(order);
            dbContent.SaveChanges(); Ok();
        }
    } { ( dbContext = MyDbContext())
        { orders = dbContext.Orders.ToList(); JsonResult(orders);
        }
    }
} : { MyDbContext _dbContext; {
        _dbContext = dbContext;
    } {
        _dbContext.Orders.Add(order);
        _dbContent.SaveChanges(); Ok();
    } { orders = _dbContext.Orders.ToList(); JsonResult(orders);
    }
} // without DI public class OrderController Controller ActionResult ( ) public Post Order order using var new return ActionResult ( ) public Get using var new var return new // with DI public class OrderController Controller private readonly ( ) public OrderController MyDbContext dbContext ActionResult ( ) public Post Order order return ActionResult ( ) public Get var return new However, I recently came across a use case where DI can be a real pain - dependency injection in inherited classes. The Problem I have recently been working a lot with the package, using it's request/request handler pattern to issue commands in the system (inspired by ). Mediatr Jason Taylor's Clean Architecture solution I typically create a RequestHandler base class that contains common dependencies and functionality. Each concrete request handler can then inherit from this base class. < , > : < , > : < >
{ {
        DbContext = dbContext;
    } IApplicationDbContext DbContext { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IApplicationDbContext dbContext protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IApplicationDbContext dbContext base dbContext Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic The problem comes when I want to add more dependencies to the base class. Now I have to go through to every single concrete request handler and update the constructor to take the new dependency as well. Fortunately, the code will not compile if I miss one, so there is no risk of a runtime error, but it is still incredibly tedious work to have to update every single request handler. Also, you can end up with very large constructors, which obscures the intention of the class. < , > : < , > : < >
{ {
        DbContext = dbContext;
        CurrentUser = currentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IApplicationDbContext dbContext, ICurrentUser currentUser protected get protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IApplicationDbContext dbContext, ICurrentUser currentUser base dbContext, currentUser Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic The Solution - Dependency Aggregates The solution to this problem is really quite simple. Rather than injecting the dependencies directly, create a new class that contains the dependencies (known as an aggregate) and inject that instead. {
    IApplicationDbContext DbContext { ; }
    ICurrentUser { ; }
} : { {
        DbContext = dbContext;
        CurrentUser = currentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; }
} < , > : < , > : < >
{ {
        DbContext = aggregate.DbContext;
        CurrentUser = aggregate.CurrentUser;
    } IApplicationDbContext DbContext { ; } ICurrentUser CurrentUser { ; } ;
} MyRequestHandler : RequestHandler<MyRequest, MyResponse>
{ { } { }
} public interface IDependencyAggregate get get public class DependencyAggregate IDependencyAggregate ( ) public DependencyAggregate IApplicationDbContext dbContext, ICurrentUser currentUser public get public get public abstract class RequestHandler TRequest TResponse IRequestHandler TRequest TResponse where TRequest IRequest TResponse ( ) public RequestHandler IDependencyAggregate aggregate protected get protected get Task<TResponse> ( ) public abstract Handle TRequest request, CancellationToken cancellationToken public ( ) : ( ) public MyRequestHandler IDependencyAggregate aggregate base aggregate Task<MyResponse> ( ) public override Handle MyRequest request, CancellationToken cancellationToken // handler logic Now if I want to add a new dependency, the only places that I need to change the code are in the DependencyAggregate class and the RequestHandler base class (I don't need to make any changes to the inherited classes). Conclusion In this post I have described a simple method for managing dependency injection in inherited classes, by creating a dependency aggregate class to inject into the base class. This ensures that new dependencies can easily be introduced with having to make changes to every inherited class. I post mostly about full stack .NET and Vue web development. To make sure that you don't miss out on any posts, please follow this blog and . If you found this post helpful, please like it and share it. You can also find me on . subscribe to my newsletter Twitter Previously published at https://samwalpole.com/handling-dependency-injection-in-inherited-classes

How to Create a Dependency Aggregate Class to Manage DI: Clearly Exemplified

I lead the technology industry business unit for a fast-growing analytics firm. And one of the perks of my job is the numerous conversations I get to have with a variety of prospects or clients. I recently read the novel ‘ by Rolf Dobelli, which deals with systematic cognitive errors of human beings as a result of our evolution. The Art of Thinking Clearly’ I enjoyed reading the book immensely, relating those cognitive errors to what I experience every day. I am selecting a few rampant cognitive errors mentioned in the book and linking them to situations I have come across. Neo-mania Def: The mania for all things new and shiny. The desire to have automation, machine models, artificial intelligence, and other such cutting-edge capabilities is intense in organizations today and probably justified. However, it’s alarming to see many of these efforts promise a very large ROI but never deliver it. marketing learning I once had an executive of a large organization say without batting an eyelid - “We have invested millions in a big data environment, and we now need to figure how to use it.” I am always disappointed when I hear things like — “Can you tell me how I can use machine learning capabilities in my business/function?” I wonder if anyone would say it if I replaced ‘machine learning’ with ‘statistics’. Conversely, the best use of ML/AI is when the need or a business problem was identified first and ML/AI emerged as a solution. Swimmer’s Body Illusion Def: Confusing the process of selection for effect. Rolf explains in the book that swimmers don’t have great bodies because they swim a lot; they are swimmers because their physique is suitable for swimming. I have seen companies falling to this illusion when they look to replicate an analytics success story from companies that seem to be doing very well and claiming that analytics is driving their success. It’s safe to say that the retail industry has the most mature analytics capability, but nobody is blaming the analytics team at Sears and Macy’s for their decline. In my opinion, most companies would succeed in their analytics initiatives if they just are more rational and use data to be just that. (Read more: Building an analytics-first organization ) Authority Bias Def: The tendency of human beings to follow authority without question, a culture most visible in religion but equally prominent in businesses. When I ask my clients why they were following a particular strategy, the responses most often indicate an authority bias. Interestingly, the bias seems more obvious when the perceived results from the strategy are less obvious. My advice to my clients has always been to focus on outcomes and not on the process. I am amazed even now that such a conversation isn’t always easy. The Paradox of Choice Def: Too many choices causing decision paralysis and subsequently leading to poor decisions. The number of , vendors, and technologies today is confounding. One would expect then that most clients would be happy with the choices they make. However, I haven’t seen a company that is entirely happy with its selection of business intelligence (BI) or . analytics solutions analytics platforms If only they hadn’t compared the tools with each other but against a prioritized list of their own needs, they would have come to a better decision. Default Effect/Status Quo Bias Def: A strong tendency to cling to the way things are, even when they put us at a disadvantage. Most tech companies with revenues of $500 million to $2 billion are hyper-focused on growth. The most important part of achieving growth is to size the market right, find the account segments to focus on, and then have the sales force or channel partners gain market share. One would imagine that these companies would have nailed this process well. I am yet to come across one. (Read more: Business Transformation — How Big Data Analytics Helps ) Alternative Blindness Def: Systematically forgetting to compare an existing offer with the next-best alternative. I have run across companies with no analytics capabilities looking to hire a team first, spending a million dollars, and losing quite a bit of time before they see any capability in action. When we provide them the alternative to spend less than half a million and have the capabilities stand up in six months, one would imagine it would be a no-brainer. It is, but only to our clients. *Please note that the terms and their descriptions are from Rolf Dobelli’s book. (This article was authored by Maruti Peri and first appeared on LinkedIn , the BRIDGEi2i blog & Datafloq .)

Master “The Art of Thinking Clearly” Before Making Your Analytics Investments!

Author:
(1) Tiffany N. Younger, The Graduate Center, City University of New York, New York, NY 10016, USA (tyounger@hunter.cuny.edu). Author: Author: (1) Tiffany N. Younger, The Graduate Center, City University of New York, New York, NY 10016, USA (tyounger@hunter.cuny.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Early Life 2. Early Life 3. Whiteness as an Institution 3. Whiteness as an Institution 4. The Triple Threat 4. The Triple Threat 5. The Academic Plantation Field 5. The Academic Plantation Field 6. The Future Is Black Women 6. The Future Is Black Women 7. Imagination as a Tool 7. Imagination as a Tool 8. Imagination through Research 8. Imagination through Research 9. Imagination through Play 9. Imagination through Play 10. Collaboration as Imagination 10. Collaboration as Imagination 11. Conclusions and References 11. Conclusions and References 4. The Triple Threat As Black women, we suffer double exploitation due to what Crenshaw (1991) calls intersectionality, which is the way anti-black policies and gender inequities result in gender anti-black racism. The third silent threat we rarely discuss is the threat of being highly formally educated in addition to being a Black woman. The intersection of gendered anti-black racism has a particular impact on darker-skinned women. As a dark-skinned Black woman initially, I was unaware of how racism intersected with colorism as a young adult. Today, as a professional, I can clearly see the way my darker skin plays a huge role in the oppression I face. I have been in numerous spaces where women of color have been at the “table”, and when I got there, I was the only darker-skinned woman of color. I have also witnessed how other women of color conducted a classroom difficult conversation the same way I have and received praise, whereas I have been told in anonymous surveys from students that my “tone is making everyone uncomfortable” when I am direct in my dialogue. Furthermore, I have witnessed advisors and faculty who were a part of my doctoral process pit me against the one other lighter-skinned Black woman who was in my cohort. Somehow, when advising me on how to go about my career and work, they would refer to the other student’s approach to her work as an example of “excellent” research and her career trajectory. While I agreed that my fellow doctoral sister-friend’s work is, in fact, stellar, the conversations made me extremely uneasy. My body told me it was more to the comparison; however, I did not have the language at the start of my career. We were both brilliant Black women, and if whiteness had won, we would not have been on the same team. Thankfully, she and I both understand abundance, and there is room for both of us; however, our intellect and collaboration are a direct threat to whiteness. Institutional whiteness seeks to divide. Centering Blackness leads to collaboration. While our Blackness and womanhood are constantly used as a DEI checkmark, our educational success and intel are viewed as a threat. Thomas et al. (2013) conceptualized the theory “pet to threat”, through a psychological study of Black women’s journey to their mid-career which highlights how bosses go from liking Black women to disliking them and deeming them a threat to the work environment and institution. Black women are the belly of the world. Hartman (2016) states, “The plantation is a womb/abyss. The plantation is the belly of the world.” Black women function as an intellectual plantation within institutions; we are the primary producers, conceptualizers, and creators within social service organizations and academia. Our creation is inductive. We seek to create new epistemological and ontological frameworks. Despite our circumstances and structural oppression, we still manage to use our imagination and vision. When I was first hired in many of my professional roles, directors and managers were excited about my passion and creative imagination. I have encountered numerous occasions where directors expressed being impressed with my academic and professional achievements despite my upbringing in a resource-deficient environment. In one instance in particular, a white male CEO interviewing me for a consulting position bluntly asked, ‘How did you make it this far given your upbringing?’. While to some, these comments might serve as a compliment, if listening closely, there is almost an undertone of “you are not supposed to survive what was designed to harm you”. The survival of Black womanhood, despite what Collins (2000) calls the “matrix of domination”, is often appalling to folks who have power. It also serves as a threat because the more we survive and thrive as Black women, the more we threaten the reality of the ideologies of dominance. Whiteness is exposed for the lies it has created and perpetuated. Resistance is crucial. Due to my on-the-ground training in advocacy and education, I have resisted within institutions and have not been forced to resign or terminated (yet). I have, however, been silenced, uninvited to meetings, and labeled a “troublemaker”. My training in policy has supported me in navigating institutional violence, but I also acknowledge that I will never be able to holistically exist in an institution that was not designed for people who look like me. Although I have not been removed from any of my employment positions, I have been surveilled and “set up”. On two occasions, I have had directors inadvertently send me emails and text messages about me with an outlined plan to watch my every move to document a mistake for human resource purposes. I have had a white male CEO include me in an email not for me and say, “we are going to get her ass”. On both occasions, I responded back to the messages, and both parties ignored my response to protect themselves. Whiteness is sloppy; when confronted, whiteness shrinks. There is a shelf life for Black women working in institutions that center power. I experienced and observed that Black women seem to have approximately eighteen months to two years within institutions before they become a threat. There is a constant dehumanization and ungendering that happens to Black women in institutions which is designed and regulated by institutional policies. Black women have historically been “ungendered” since US chattel slavery. The constant ungendering served as a justification and institutional functioning of US chattel slavery (Spillers 2013). This ungendering happens within institutions the moment darker-skinned Black women’s passion is labeled aggressive or “unprofessional”. Our intellect, leadership, and drive for change are viewed as “having an agenda”, when in fact I have admitted to an agenda during the interview process. The agenda is racial and gender equity. What was previously celebrated and sought after is now controlled, deemed unprofessional, and an ultimate threat to the institution. Whiteness perpetuates harm to protect itself. Gem #4. Gather a team of four to seven people who can remind you of who you are, challenge you, and hold you accountable. If whiteness seeks to change the way you speak, dress, or “carry yourself” guised as professionalism, it is dehumanizing. Make sure the critique is based on tangible work outcomes, things you can change without stripping who you are. Gem #4. Gather a team of four to seven people who can remind you of who you are, challenge you, and hold you accountable. If whiteness seeks to change the way you speak, dress, or “carry yourself” guised as professionalism, it is dehumanizing. Make sure the critique is based on tangible work outcomes, things you can change without stripping who you are. Gem #4. Gather a team of four to seven people who can remind you of who you are, challenge you, and hold you accountable. If whiteness seeks to change the way you speak, dress, or “carry yourself” guised as professionalism, it is dehumanizing. Make sure the critique is based on tangible work outcomes, things you can change without stripping who you are. Gem #4. Gather a team of four to seven people who can remind you of who you are, challenge you, and hold you accountable. If whiteness seeks to change the way you speak, dress, or “carry yourself” guised as professionalism, it is dehumanizing. Make sure the critique is based on tangible work outcomes, things you can change without stripping who you are. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Too Brilliant, Too Black: The Institutional Fear of Black Womanhood

It was in early 2025 that I initiated my own blog, OpenExploit.in, with one simple aim: to make cybersecurity easy to learn and available to all—particularly students and curious minds who are new to this field. I decided to host my blog using Ghost CMS and pledged to update it on a regular basis, discussing what I already know and what I'm currently learning. It's been a thrilling, at times daunting, but enormously rewarding ride. In this article, I'd like to share what I've learned up until now from setting up and maintaining a cybersecurity blog—from technical configuration to publishing content, interacting with readers, and how to stay motivated. If you're considering setting up your own blog or simply wondering what it's like, keep reading. Starting Was the Hardest Part As with all things in life, the initial step was the most difficult one. I went weeks deliberating over names, designs, and tools. I wanted everything to be "perfect" before launching. But after some time, I came to understand that perfection is the arch-nemesis of progress. So I settled on a name that was fitting—OpenExploit.in—and used Ghost CMS for its sleek, minimal look and speed. Lesson learned: Don't wait for perfection. Start small, and improve along the way. Choose the Right Platform Early I tried a few: WordPress, Blogger, and Ghost. I chose Ghost CMS because: It's open-source
It's fast
The writing experience is distraction-free
It's ideal for developers and technical blogs It's open-source It's fast The writing experience is distraction-free It's ideal for developers and technical blogs Setting up Ghost on a server was a bit of a learning curve, but it taught me about server configuration, domain management, SSL, and more. In hindsight, that technical knowledge became part of my own cybersecurity learning journey. Lesson learned: Choosing a platform that fits your technical comfort level matters a lot. You’ll spend a lot of time with it, so make sure it feels right. Writing Is a Skill—And It Gets Better With Practice I was anxious writing my initial post. Would anyone read it? Would anyone judge it? What if I got something wrong? The reality? None of that initially. I just had to write. My initial post was simple, detailing how to know if a website is safe to click on. Not ideal, but useful. As I wrote more, I found myself becoming more skilled at making things simple. The more I wrote, the surer I became. Lesson learned: Writing is similar to exercising. The more you exercise it, the more skilled you become. And the more clearly you write, the better understood you will be by people. Simplify Complex Subjects Cybersecurity can get very technical. When I was in the mode of writing for beginners, I found that making things simple and accurate in wording is an art. You can't simply plagiarize from a book. You need to: Use analogies
Avoid jargon
Break content into bite-sized sections
Include visuals or code snippets
Point to further resources Use analogies Avoid jargon Break content into bite-sized sections Include visuals or code snippets Point to further resources For instance, rather than merely stating "SQL injection enables attackers to manipulate database queries," I employed an everyday analogy: "It's like writing your own ending to someone else's sentence." Lesson learned: When teaching others, you're compelled to make your own thinking simple. And that's a good thing. Consistency > Perfection One of the things I wanted to do was post frequently—daily, if possible. Life doesn't always agree, but I attempted to be consistent. Some posts were lengthy and informative; others were brief tips or references to good resources. Consistency allowed me to gain momentum, remain motivated, and gradually accumulate a reader base. Lesson learned: Posting a small useful article every few days is preferable to waiting weeks to put out a single "perfect" post. There's a Huge Community Out There There are cybersecurity enthusiasts all over—on Twitter (X), LinkedIn, Reddit, Discord, and YouTube. After I began posting my posts and interacting with other people, I knew I was part of an enormous community. Individuals began responding, offering feedback, posing questions, and even proposing new subjects. That interaction made me feel connected and encouraged me to continue. Lesson learned: Don't blog in isolation. Share your post, engage in discussions, assist others. You'll receive much in return. Use Multimedia: Code, Images, and Videos Cybersecurity is tactile and graphical. So I began including: Code snippets (e.g., Python scripts, Nmap scans)
Screenshots of software such as Wireshark or Burp Suite
YouTube Shorts or video tutorials Code snippets (e.g., Python scripts, Nmap scans) Screenshots of software such as Wireshark or Burp Suite YouTube Shorts or video tutorials That not only helped readers understand better but also increased the time they spent on my posts. Now, I’m planning to embed more demo videos and maybe even start a companion YouTube channel. Lesson learned: People learn in different ways. Give them multiple options—text, video, images, code. It’s Okay to Not Know Everything Initially, I was anxious: "What if I mess something up?" But the thing is, nobody knows everything about cybersecurity. It's too vast, too dynamic. Rather than acting like an expert, I was a learner who explained what they were learning. That made me more down-to-earth and authentic. I even posted about things I didn't completely understand yet—just going through my learning process. Lesson learned: Be real. It's alright to say, "I'm still learning this." People value honesty. SEO, Titles, and Thumbnails Matter As much as I emphasized the content, I finally realized that titles, descriptions, and images count as well. A blog post with the title "Basics of Cybersecurity" is less interesting than one with the title "5 Cybersecurity Myths You Still Believe (And What's Actually True)." Good SEO makes people discover your content. I began learning: How to write catchy headlines
How to use keywords naturally
How to write summaries and meta descriptions
How to design simple but clickable thumbnails How to write catchy headlines How to use keywords naturally How to write summaries and meta descriptions How to design simple but clickable thumbnails Lesson learned: Content is king, but presentation is queen. Your Blog Is Your Resume I didn't know this initially, but my blog was now part of my personal brand. When I was applying for jobs or meeting people online, I could simply send them a link to OpenExploit.in. It demonstrated that I: Know how to write about technical subjects
Can dedicate myself to a project
Keep up to date with trends
Care about assisting others Know how to write about technical subjects Can dedicate myself to a project Keep up to date with trends Care about assisting others Lesson learned: A blog isn't a side project—it can be a career booster. Final Thoughts Opening OpenExploit.in has been one of the greatest things I've ever done. It's helped me become a better communicator, a more self-assured learner, and part of an incredible cybersecurity community. If you've ever considered opening your own blog—do it. Don't worry about being perfect or knowing everything. Just begin. Write what you know. Someone out there needs to know it. Thanks for reading. And if you're on the same journey—let's connect.

Here’s Everything I Learned Building a Blog to Teach Cybersecurity

https://open.spotify.com/episode/5l6zZ3J3HKXQLnncswYDhU?si=VoSLwHNwQEmq88AAMB2FKw&embedable=true https://open.spotify.com/episode/5l6zZ3J3HKXQLnncswYDhU?si=VoSLwHNwQEmq88AAMB2FKw&embedable=true You've been lied to about excellence. They told you it's about grinding harder. About perfecting systems. About optimizing what already exists. That's not excellence. That's just the hamster wheel painted a different color. True excellence isn't about running the race better. It's about changing the track entirely. Richard Feynman didn't solve impossible physics problems by working harder than other scientists. He invented a new visual language – simple stick figure diagrams that looked like a child's doodles but solved what complex equations couldn't. Feynman wasn't just any physicist – he was a Nobel Prize winner who helped develop the atomic bomb on the Manhattan Project and later became one of the most celebrated scientific minds of the 20th century. What made him extraordinary wasn't just his intelligence but his completely different approach to problem-solving. While his colleagues at CalTech and Princeton buried themselves in impenetrable mathematics, Feynman created a visual system that made quantum mechanics intuitive. These "Feynman diagrams" revolutionized physics and are still used today, allowing scientists to calculate what happens when particles interact without drowning in pages of equations. Excellence doesn't play by the rules. It rewrites them. This is what Aristotle missed when he said: "Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution." "Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution." "Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution." I love it. But he nailed the ingredients while missing the recipe. Excellence isn't just intentional – it's subversive. It questions assumptions everyone else has forgotten are assumptions. And that's where your opportunity hides. The Invisible Box Trapping Your Potential Your industry has "best practices." Established methods. Proven playbooks. These aren't wrong. They're worse than wrong. They're average. Every time you follow a best practice, you're guaranteeing mediocrity. By definition, if everyone does it, it creates zero advantage. Excellence begins precisely where best practices end. Your competition lives inside an invisible box labeled "how things are done." They think they're being strategic by optimizing inside that box. They're actually ensuring their own mediocrity. You see it in how they think: "Let's do what works, but better"
"That's just how our industry operates"
"We need to follow established patterns" "Let's do what works, but better" "That's just how our industry operates" "We need to follow established patterns" These thoughts aren't harmless. They're excellence killers. The real question isn't "How can I do this better than my competition?" It's "What if the fundamental assumptions my competition believes are actually holding them back?" The difference isn't subtle. It's everything. And this mindset shift opens a door to the next level of your potential. The Painful Path to Seeing Reality You can't create excellence without first seeing reality clearly. Most people can't handle reality. They build elaborate cushions between themselves and hard truths. These cushions feel comfortable but make excellence impossible. Ray Dalio understood this when building Bridgewater Associates – now the world's largest hedge fund. He created "radical transparency," where painful truths aren't just accepted, they're actively hunted down. Employees call out weaknesses, challenge bosses directly, and ruthlessly dissect ideas looking for flaws. Many brilliant people can't handle it and quit. Those who stay consistently outperform everyone else in the market. Why? Because their excellence threshold is determined by their truth threshold. Your capacity to achieve excellence is directly proportional to your capacity to face uncomfortable reality. How often do you actively seek feedback that makes you cringe? How often do you say what needs to be said when silence would be easier? That's not just your comfort zone boundary. It's your excellence ceiling. And raising that ceiling requires a simple but painful choice: choose truth over comfort, especially when nobody's watching. But what if your biggest limitations aren't what's holding you back, but actually pushing you forward? The Counterintuitive Power of Constraints Here's what nobody tells you about excellence: it rarely comes from abundance. It emerges from strategic limitation. We're conditioned to believe that more resources, more time, and more freedom naturally lead to better outcomes. Reality shows the opposite. The Apollo 13 mission turned potential disaster into NASA's finest achievement not despite constraints but because of them. When an oxygen tank exploded, engineers had to build a carbon dioxide filter using only what the astronauts had available – duct tape, plastic bags, and a flight manual cover. Those limitations forced creative thinking that unlimited resources never would have prompted. Your constraints aren't obstacles to excellence. They're the raw material for it. What limitation in your business are you fighting that might actually be your greatest potential advantage? The founder complaining about limited funding might be sitting on the constraint that forces genuine innovation rather than copy-paste solutions. The creator frustrated by a small audience might have the perfect conditions for developing truly original work rather than playing to the crowd. Your path to excellence isn't eliminating constraints. It's leveraging them deliberately. Your path to excellence isn't eliminating constraints. It's leveraging them deliberately. But navigating this path requires holding two seemingly opposite ideas in your mind at once. The Paradox You Must Embrace Excellence requires holding two contradictory ideas simultaneously: You must master existing rules while preparing to break them completely. Most people choose one path or the other. They either become obedient practitioners of conventional wisdom or arrogant disruptors who ignore foundations. Both paths lead to mediocrity. True excellence emerges at the intersection of deep knowledge and radical reimagining. This isn't just philosophical – it's practical. Your journey to excellence follows this paradoxical path: Master the fundamentals more deeply than your peers
Identify the hidden assumptions within those fundamentals
Challenge the assumptions most central to your field
Create a new approach that makes the old one obsolete Master the fundamentals more deeply than your peers Identify the hidden assumptions within those fundamentals Challenge the assumptions most central to your field Create a new approach that makes the old one obsolete This is why true innovations rarely come from complete outsiders or complete insiders. They come from those who understand the system deeply enough to see exactly where it fails. Your next level isn't choosing between tradition and disruption. It's using tradition as the launch pad for disruption. But even with this knowledge, there's a more fundamental challenge you'll need to overcome. The Real Barrier Standing In Your Way The hardest part of excellence isn't talent, knowledge, or resources. It's courage. Not physical courage, but the social courage to be misunderstood – often for extended periods. When Jeff Bezos outlined Amazon's strategy to prioritize growth over profits, Wall Street called it "Amazon.bomb" and predicted imminent failure. For years, Bezos was portrayed as delusional for investing in infrastructure instead of extracting quick profits. His response reveals everything: "We are willing to be misunderstood for long periods of time." "We are willing to be misunderstood for long periods of time." This willingness to be temporarily misunderstood is necessary for eventual recognition. Most people would rather be understood for their mediocrity than misunderstood for their excellence. Your comfort with criticism directly affects what you can achieve. Every truly excellent idea will initially look wrong, excessive, or misguided to most people. How much social discomfort can you tolerate while pursuing what you know is right? That's your excellence capacity. And expanding that capacity might be the single highest-leverage investment you can make. But excellence isn't just about having the courage to stand apart—it's also about understanding the nature of the journey itself. The Moving Target That Changes Everything Excellence isn't a fixed destination. It's a continuous process of improvement. This seems frustrating at first. Why pursue what you can never fully achieve? Because excellence isn't about the arrival at some fantasy finish line. It's about who you become in the pursuit. The Japanese concept of kaizen embodies this perfectly. It's not about reaching some ideal final state. It's about creating a perpetual gap between where you are and where you could be, then continually closing that gap while knowing it will never fully disappear. Toyota didn't revolutionize manufacturing by reaching some endpoint. They built systems that turned yesterday's excellence into today's baseline, and today's excellence into tomorrow's minimum standard. This orientation toward the unattainable changes how you make decisions. Instead of asking "Is this good enough?" you ask "Is this better than before?" The standard isn't some fixed threshold – it's your previous best. This approach isn't perfectionism, which is fear-based and stops progress. It's a productive mindset that recognizes potential improvements while still delivering your current best work. And it transforms how you approach every aspect of your work. But to put this mindset into practice, you need a practical tool that cuts through the noise. The Question That Unlocks Everything Most of your life and your decisions happen on autopilot. You follow habits, industry norms, and established patterns without much conscious thought. Excellence requires interrupting this autopilot with a question that changes everything: "What would this look like if it were excellent?" "What would this look like if it were excellent?" "What would this look like if it were excellent?" Not good. Not competitive. Not impressive. Excellent. Excellent. This question reframes decisions from "What should I do?" to "What could this become?" When Jiro Ono asked this about sushi, he didn't add more exotic ingredients. He stripped everything back to basics, obsessing over rice temperature and fish quality in ways other chefs thought excessive. The result? A tiny restaurant in a subway station that earned three Michelin stars and worldwide recognition. Ask this question about your product, your marketing, your customer experience, your daily habits. The difference between your vision and current reality shows you where to improve. And closing that gap requires a fundamental recalculation of risk—one that most people get completely wrong. The Risk Equation That Changes Everything Most people believe excellence is inherently riskier than mediocrity. After all, shooting for average means fewer chances to fail dramatically. This calculation misses a crucial truth: The risks of excellence and mediocrity are fundamentally asymmetric. The downside of pursuing excellence is limited. Worst case? You fail and end up with something merely good. The downside of accepting mediocrity is unlimited. You guarantee you'll never create something extraordinary. Excellence has bounded risk and unbounded reward. Mediocrity has bounded reward and unbounded risk. Excellence has bounded risk and unbounded reward. Mediocrity has bounded reward and unbounded risk. When Howard Schultz reimagined Starbucks from a coffee bean retailer to a cafe experience, the company's board strongly opposed the idea. He had to leave and start his own company (Il Giornale) to prove the concept before eventually acquiring Starbucks and implementing his vision. Had he failed, he would have created a few nice coffee shops. By succeeding, he transformed global coffee culture and built a company worth over $100 billion. Most people systematically overestimate the risks of excellence while underestimating the risks of mediocrity. This miscalculation leads to the tragic scenario where playing it safe becomes the riskiest strategy of all. Your next level requires correcting this equation in your own decisions. But ultimately, all of these insights really just come down to one fundamental truth about excellence. The Choice That Defines Everything Excellence isn't something that happens to you. It's something you choose. That choice rarely comes as a single dramatic moment. It's made in small decisions that accumulate over time: The decision to question an assumption when accepting it would be easier. The decision to question an assumption when accepting it would be easier. The decision to start over when what you've created is good but not excellent. The decision to start over when what you've created is good but not excellent. The decision to maintain a standard even when no one else would notice the difference. The decision to maintain a standard even when no one else would notice the difference. These decisions seem small in isolation. Collectively, they define the difference between work that's forgotten and work that endures. The gap between where you are and excellence isn't a skill gap or a resource gap. It's a decision gap. You know what excellence looks like. You just don't choose it when it costs too much. As Aristotle reminds us: Excellence is never an accident. It's a choice. Your choice. And that choice is available to you right now, in whatever you're working on. What will you choose today? Thank you for reading. – Scott

Is Excellence An Accident?

Is uitnemendheid 'n ongeluk?

¿Excelencia ukax mä Accidente ukhamäpachati?

L'excel·lència és un accident?

¿La excelencia es un accidente?

L’excellence est-elle un accident?

Ինչպե՞ս է սխալը սխալը։

エクセレンスは事故?

Excellence - бұл апат па?

តើឧត្តមភាពជាឧប្បត្តិហេតុទេ?

Vai Excellence ir nelaimes gadījums?

آیا Excellence یو حادثه ده؟

A excelência é um acidente?

I want you to take a moment to imagine not having a bank account, credit card, or access to loans. How would you save money, invest, or handle financial emergencies? A World Bank report from 2022 says about 1.4 billion people worldwide are unbanked. The report approximates that 71% of unbanked adults live in rural areas, which often lack adequate banking infrastructure, and it also highlights that a significant barrier to financial inclusion is the absence of formal identification. In Sub-Saharan Africa alone, over 100 million unbanked adults lack IDs, and globally, around 30% of unbanked individuals cite insufficient documentation as a major obstacle to accessing financial services. In Sub-Saharan Africa alone, over 100 million unbanked adults lack IDs, and globally, around 30% of unbanked individuals cite insufficient documentation as a major obstacle to accessing financial services. Financial inclusion means making financial services available and affordable for everyone, no matter their income or where they live. Besides helping individuals, inclusive finance supports economic stability, reduces poverty, and promotes social mobility. Even though there are clear benefits, traditional banks often ignore marginalized groups. High account fees, strict credit requirements, limited outreach, and no credit history keep many people stuck in poverty. The Gluwa Mission Mission Statement Gluwa's mission is to make financial services available to everyone by using new blockchain technology. They aim to connect traditional financial systems with underserved communities around the world. As a decentralized financial platform, Gluwa links global funds to investment opportunities in emerging markets, giving users the tools and resources they need to succeed, no matter their background, nationality, income level, or gender. The company does this by combining blockchain technology with traditional financial services to create a global financial system that is open to everyone. By keeping track of users' payment histories, like utility bill payments, on a public blockchain, Gluwa helps people without traditional banking access build credit profiles that can be verified. This system works with a stablecoin-based financial network linked to regular currency, making it easier for people in underserved areas to do financial transactions and promoting financial inclusion. Gluwa shows its dedication to financial inclusion through partnerships with different organizations, including an agreement with the Central Bank of Nigeria to boost the use of Nigeria’s central bank digital currency (CBDC), the eNaira. This partnership aims to make fintech lending easier and verify transactions, improving financial access for millions. agreement with the Central Bank of Nigeria to boost the use of Nigeria’s central bank digital currency (CBDC) With these efforts, Gluwa aims to create a fairer global economy by offering open and clear financial systems that help underserved communities. Addressing the Challenges How does Gluwa tackle these big challenges? By using blockchain technology, Gluwa gets rid of traditional obstacles like high transaction fees, strict credit checks, and limited geographic access. In simple terms, Gluwa isn't just following different rules—it's changing the game completely. Gluwa tackles financial exclusion with several key strategies: Lowering Transaction Costs: Traditional financial systems have high fees that make it hard for low-income people to use services. Gluwa uses blockchain for direct, peer-to-peer transactions, cutting out middlemen and reducing costs, making services cheaper for underserved communities.


Making Credit Easier to Get: Strict requirements and lack of credit history often block access to credit. Gluwa's Creditcoin blockchain records loan transactions, helping people build verifiable credit histories, which improves their credibility and access to loans.


Extending Geographic Access: Gluwa's platform bypasses limited banking infrastructure by providing digital financial services through mobile devices, enabling people in remote areas to access financial systems without needing physical banks.


Building Strategic Partnerships: Gluwa works with governments and financial institutions, such as teaming up with Nigeria's Central Bank to connect blockchain with the eNaira, improving financial access and transaction verification.


Using Scalable Technology: Gluwa uses Microsoft Azure for dependable and scalable cloud solutions, ensuring efficient blockchain services worldwide. Lowering Transaction Costs: Traditional financial systems have high fees that make it hard for low-income people to use services. Gluwa uses blockchain for direct, peer-to-peer transactions, cutting out middlemen and reducing costs, making services cheaper for underserved communities. Lowering Transaction Costs: Traditional financial systems have high fees that make it hard for low-income people to use services. Gluwa uses blockchain for direct, peer-to-peer transactions, cutting out middlemen and reducing costs, making services cheaper for underserved communities. Lowering Transaction Costs Making Credit Easier to Get: Strict requirements and lack of credit history often block access to credit. Gluwa's Creditcoin blockchain records loan transactions, helping people build verifiable credit histories, which improves their credibility and access to loans. Making Credit Easier to Get: Strict requirements and lack of credit history often block access to credit. Gluwa's Creditcoin blockchain records loan transactions, helping people build verifiable credit histories, which improves their credibility and access to loans. Making Credit Easier to Get: Extending Geographic Access: Gluwa's platform bypasses limited banking infrastructure by providing digital financial services through mobile devices, enabling people in remote areas to access financial systems without needing physical banks. Extending Geographic Access: Gluwa's platform bypasses limited banking infrastructure by providing digital financial services through mobile devices, enabling people in remote areas to access financial systems without needing physical banks. Extending Geographic Access Building Strategic Partnerships: Gluwa works with governments and financial institutions, such as teaming up with Nigeria's Central Bank to connect blockchain with the eNaira, improving financial access and transaction verification. Building Strategic Partnerships: Gluwa works with governments and financial institutions, such as teaming up with Nigeria's Central Bank to connect blockchain with the eNaira, improving financial access and transaction verification. Building Strategic Partnerships Using Scalable Technology: Gluwa uses Microsoft Azure for dependable and scalable cloud solutions, ensuring efficient blockchain services worldwide. Using Scalable Technology: Gluwa uses Microsoft Azure for dependable and scalable cloud solutions, ensuring efficient blockchain services worldwide. Using Scalable Technology With these focused efforts, Gluwa breaks down traditional barriers to financial inclusion, giving underserved communities the tools and opportunities they need for economic growth. How Gluwa Works. Blockchain isn't just about cryptocurrency; it's a decentralized system that makes transactions secure, clear, and unchangeable. For financial inclusion, this means people without traditional credit histories can create financial identities safely and openly. Blockchain changes trust from something only institutions have to something everyone can access. It also cuts out the need for traditional middlemen like banks and credit bureaus, lowering transaction costs and allowing direct, person-to-person financial interactions. This opens up financial activities to underserved communities, especially in remote areas, who couldn't participate before due to distance or high costs. Additionally, blockchain lets users build verifiable credit histories by clearly recording payment activities, greatly improving their credibility and access to financial opportunities. Gluwa’s platform uses a decentralized blockchain network, allowing users to send, receive, and safely store money worldwide without the usual obstacles from traditional banks. With tools like Creditcoin, users can create clear and verifiable credit histories, boosting their financial credibility. Gluwa also partners with local organizations, NGOs, government agencies, and innovative fintech providers to expand its infrastructure. These partnerships make sure the system is strong, easy to access, and able to reach even the most remote and underserved areas, promoting smooth financial inclusion everywhere. Gluwa's Broader Impact Gluwa's projects are set to greatly improve financial access in areas with limited banking services, especially in parts of Africa, Southeast Asia, and Latin America. For example, in March 2024, Gluwa teamed up with the Central Bank of Nigeria (CBN) to promote the use of the eNaira, Nigeria's digital currency. This partnership uses blockchain technology to improve financial access and encourage innovation in the area. Additionally, Gluwa is looking into partnerships in other African countries, like Liberia, to create digital currencies and satellite internet services, showing its dedication to advancing digital financial services across the continent. By adding blockchain solutions to current financial systems, Gluwa encourages traditional banks to rethink and possibly improve how they offer services. Partnerships like the one with the CBN show how blockchain can support and enhance regular banking systems, which could lead to more inclusive financial services. Beyond finance, Gluwa's principles—transparency, accessibility, and decentralization—can also be used in areas like education and healthcare. For instance, blockchain can securely share educational records worldwide or provide decentralized healthcare funding, spreading the benefits of financial inclusion to other important parts of society. Conclusion Financial inclusion is a must, not a choice. Gluwa is an important step in helping underserved communities get access to financial services. As decentralized financial services improve, they could change not just finance but many parts of our lives. The real question isn't if financial inclusion is needed—it definitely is—but if we're ready to accept solutions like Gluwa, which can open doors that traditional finance has kept shut.

Helping the Unbanked: How Gluwa Supports Global Financial Access

Author:
(1) Jerker Denrell, University of Warwick (jdenrell@gmail.com) Author: Author: (1) Jerker Denrell, University of Warwick (jdenrell@gmail.com) Table of Links Abstract and 1. Introduction 2. Illustration 3. Intuition 4. General Theorem about Biased Averages 5. Alternative Learning Models 6. Bayesian Updating 7. Implications for Understanding Learning Appendix and References Abstract. The Hot Stove Effect is a negativity bias resulting from the adaptive character of learning. The mechanism is that learning algorithms that pursue alternatives with positive estimated values, but avoid alternatives with negative estimated values, will correct errors of overestimation but fail to correct errors of underestimation. Here, we generalize the theory behind the Hot Stove Effect to settings in which negative estimates do not necessarily lead to avoidance but to a smaller sample size (i.e., a learner selects fewer of alternative B if B is believed to be inferior but does not entirely avoid B). We formally demonstrate that the negativity bias remains in this set-up. We also show there is a negativity bias for Bayesian learners in the sense that most such learners underestimate the expected value of an alternative. Abstract 1. Introduction Learning from experience does not necessarily generate unbiased beliefs, partly due to psychological biases but also due to biases in the information learners’ sample and get exposed to. One important bias in sampling is the so-called "hot stove effect" (Denrell & March 2001) which refers to the asymmetry in error correction generated by adaptive learning processes. The key idea is that the tendency to avoid alternatives with unfavorable past outcomes generates a biased set of experiences. Alternatives that are underestimated - believed to be worse than what they are – are unlikely to be tried and sampled again, which implies that errors of underestimation are unlikely to be corrected. Alternatives that are overestimated - believed to be better than what they are - are likely to be tried and sampled again which implies that errors of overestimation are likely to be corrected. This asymmetry in error correction generates a biased set of experiences that, in turn, can give rise to biased judgments (Denrell, 2005), including in-group bias and apparent risk-averse behavior (Denrell, 2005, 2007). There is good experimental support for the hot-stove effect at the individual level and researchers in psychology have relied on the hot-stove effect to explain regularities in risk taking in experimental studies (Erev & Roth, 2014) and why people underestimate the trustworthiness of others (Fetchenhauer & Dunning, 2014). Researchers in finance (Dittmar & Duchin, 2016) have used field data to demonstrate that the hot stove effect can explain the risk-taking behavior of executives. The hot stove effect also has important implications for information aggregation and online reviews: If consumers avoid products with poor reviews and consumers review products they buy, negative reviews will be more persistent than positive reviews, generating biased averages (Mens et al., 2018). Past theoretical work on the hot stove effect has assumed that negative experiences may lead to avoidance, that is, the alternative has not been tried at all (Denrell, 2005, 2007). Clearly, if no more information is available, a negative impression will persist. In many settings, however, a negative belief or impression may not lead to avoidance of the alternative, but rather to a smaller sample size. An animal with a more favorable impression of the energy content of a plant of type A than of a plant of type B may search for plants of type A. During this search for plants of type A, some plants of type B may be incidentally found. The result is that the animal samples more plants of type A (because the search is focused on such plants) than of type B, but the animal does not avoid plants of type B but simply samples fewer of them. Similarly, a firm may prefer to hire graduates from university A, but may hire some, although fewer, graduates from university B if there are not enough graduates from A that accept its offers. In this paper, I generalize the theory behind the hot-stove effect and show that it holds even if a negative impression only leads to a reduction in the sample size, not necessarily to avoidance. Specifically, I show that the final belief will be biased for a broad class of learning algorithms in which the sample size is a function of the past belief. If the sample size is higher and the past belief was more positive, there is a negativity bias: The final belief will be lower than the expected value of the random variable the learner is learning about. This result also applies to taking averages: the average of a sample will be biased if the total sample size is a function of the average based on an initial subset. The bias will be eliminated in the long run as the number of samples increases, but in the short run it can be significant. I also examine whether the bias remains for a Bayesian learner. I show that there is no bias on average for a Bayesian learner: the average belief is equal to the expected value of the random variable the learner is learning about. However, I also show that most Bayesian learners will underestimate the variable they are learning about if the sample size is an increasing function of the initial belief. These results imply that a large class of sensible and adaptive learning processes can be expected to generate biased beliefs, even if decision-makers process the available information in a seemingly unbiased way (i.e., taking averages). Indeed, even rational Bayesian learners will tend to underestimate the expected value of an alternative (i.e., most will do so) if the total sample size is higher when the initially observed payoffs are high. Adaptive sampling processes are common and often necessary to reduce search costs. It is not sensible, for example, to continue to sample an alternative a fixed number of times if initial trials reveal that this alternative has a much lower payoff than other available alternatives. The results in this paper show that even unbiased processing of information generated by such sampling policies can generate seemingly biased beliefs. Adaptive sampling policies thus offer an alternative explanation of biases in beliefs, such as a tendency to underestimate the extent to which others are trustworthy. 2. Illustration 3. Intuition As this example shows, adaptive sampling implies that negative first-period averages are more ’persistent’: they are given a larger weight than positive firstperiod averages. Because negative and positive first-period averages of the same magnitude are equally likely, when the payoff distribution is normal with mean zero (this distribution is symmetric around zero), the greater persistence of negative first-period averages explains the overall negative bias. The impact of variance can also be understood in a similar way. If the payoff distribution is more variable, the first period averages will tend to differ more from zero, both in the positive and negative directions. Positive first-period averages will tend to regress more to the mean (zero) than negative averages. When the negative first-period averages are more extreme because the variance is greater, the result is a stronger bias. Of course, the bias will be eliminated in the long run, as the number of samples increases since the average of n samples of a random variable X will converge to its expected value. In the short run, however, the bias can be significant. n 4. General Theorem about Biased Averages The bias generated by an adaptive sampling size policy does not only hold for the normal distribution and the specific binary sampling policy considered above (high above zero, low below), but it holds for any distribution and a large class of adaptive sampling policies. Proof: See the Appendix. Proof For unimodal symmetric distributions, centered on the expected value E[x] = u, it can also be shown that a majority of the averages, after the second period, are below u if higher averages in the first period lead to larger sample sizes in the second period: Proof: See the Appendix. Proof 5. Alternative Learning Models So far we have assumed that the learner computes the average of the observed payoffs, but a similar bias holds for several other learning models. Suppose, for example, that the learner gives more weight to the most recently observed payoff. This results in a similar bias: Proof: See the Appendix. Proof 6. Bayesian updating We now show that Bayesian updating remains unbiased in this latter sense, even if the sample size in period two depends on the belief after period one. This is true for any distribution: Proof: See the Appendix. Proof Thus, even if Bayesian learners are learning about a symmetric distribution and they have a prior that is symmetric around zero (m = 0), most learners will have a negative belief after sampling. This is due to the adaptive sampling policy. The intuition is similar to why the learning policy that takes the average is biased: negative initial beliefs are more persistent because the learner will then not take many additional samples, and the initial few negative observations will be weighted heavily. Note that such a bias would not occur if the learner followed a fixed sampling policy and decided, at the outset, how many samples to take. If the sample size was fixed, the belief would be equally likely to be positive or negative, when learning about a normally distributed payoff with a mean taken from normally distributed prior with mean zero. Note also that when sampling is adaptive, all Bayesian learners know that only 50% of the means are negative. Still, most Bayesian learners believe that the mean they observe is negative. That a majority of Bayesian learners end up with a negative belief may seem paradoxical since on average there is no bias: E[b2] = 0 when m = 0, i.e., the average belief, averaging over all Bayesian learners, is equal to the mean of the prior. The paradox is resolved by noting that the learners who believe that the mean is negative are less confident in this estimate than the learners who believe that the mean is positive and therefore took a larger sample in the second period. This results in a skewed distribution of beliefs after the second period. Most beliefs are below m = 0 but those below m = 0 are less likely to be extreme beliefs than beliefs above m = 0 since beliefs below m = 0 are based on smaller sample sizes than those above m = 0. This can be seen in Figure 1. 7. Implications for Understanding Learning The bias resulting from adaptive sampling policies implies that even seemingly unbiased learning algorithms, such as averaging or Bayesian updating, can result in biased beliefs, at least in the sense that most learners underestimate (or overestimate) an alternative. This offers an alternative explanation of some judgment biases; an explanation that does not require a psychological bias that assumes biases in information processing. For example, suppose that it is observed that a firm tends to underestimate the productivity of graduates from universities. Such a negativity bias. can be explained by an adaptive sampling policy (the firm tends to hire fewer people from universities it has had a worse experience with) and does not require motivated reasoning or a cognitive bias. Appendix Proof of Theorem 2: Proof of Theorem 2: Without loss generality we only consider cases when u = 0. Aunimodal random variable symmetric around the mode k can always be represented as x = k + ε where ε has a unimodal distribution symmetric around zero. We first prove the following Lemma The proof now follows by induction, because the sum of k independent random variables with a unimodal distribution symmetric around u is also unimodal symmetric centered on u (Shaked & Shantikumar (2007), page 173). Consider a fixed value of r. Suppose r > 0. The average after the second period becomes less than zero whenever The probability that the average is below zero after the second period is thus Altogether, the probability that the average is below zero after the second period equals Altogether, the probability that a positive belief after period one turns into a negative belief after period two equals where f(z) is the density of the sum of the payoffs in the first period which is a normal distribution with mean zero. Altogether, the probability that a negative belief after period one turns into a positive belief after period two, Pr(− → +), equals After the variable substitution, z = −y, this integral equals where we have used the fact that f(−y) = f(y). We wish to show that Pr(+ → −) > Pr(− → +), which requires us to show that over the positive domain of z. To do so, note first that the derivative of with respect to w is References Denrell, J. (2005). Why most people disapprove of me: Experience sampling in impression formation. Psychological Review, 112, 951–978. Denrell, J. (2007). Adaptive learning and risk taking. Psychological Review, 114, 177–187. Denrell, J., & March, J. (2001). Adaptation as information restriction: The hot stove effect. Psychological Review, 12, 523–538. Dittmar, A., & Duchin, R. (2016). Looking in the rear-view mirror: The effect of managers’ professional experiences on corporate financial policy. Review of Financial Studies, 29, 565–602. Erev, I., & Roth, A. (2014). Maximization, learning, and economic behavior. Proceedings of the National Academy of Sciences, 111, 10818–10825. Fetchenhauer, D., & Dunning, D. (2014). Why so cynical?: Asymmetric feedback underlies misguided skepticism regarding the trustworthiness of others. Psychological Science, 21, 189–193. Mens, G. L., Kovács, B., Avrahami, J., & Kareev, Y. (2018). How endogenous crowd formation undermines the wisdom of the crowd in online ratings. Psychological Science, 29, 1475–1490. Shaked, M., & Shantikumar, J. G. (2007). Stochastic orders. New York, NY: Springer. Williams, D. (1991). Probability with martingales. Cambridge, UK: Cambridge University Press. Author:
(1) Jerker Denrell, University of Warwick (jdenrell@gmail.com) Author: Author: (1) Jerker Denrell, University of Warwick (jdenrell@gmail.com) This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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