Tag Archives: creativity

Self Efficacy, and the way forward

While I’ll always be a fan of the OODA loop, a great conceptual model of human cognition, it does not help me in predicting outcomes. That’s why I generalized Horn et al to create a domain-knowledge/general-ability/motivation/behavior model of performance. Writing about this will be its own challenge, however.

This comes at about the same time I have discovered self-efficacy, an incredibly powerful tool first developed by Albert Bandura. Self-efficacy blows away concepts such as self-esteem, self-concept, self-definition, identity, and so on, and also better explains findings described by Expectancy-Value Theory, Goal Theory, and so on.

Self-efficacy boils down to a set of simple questions, all of which have this form: How confident are you that you can perform a specific action in order to achieve a goal, as of now. Self-efficacy is obviously beyond behavioralism, because such self-reports were frowned on by the behavioralists that Skinner. However, it is much more action-centered than other ‘cognitive’ or ‘constructive’ theories. What you feel, how proud you are, what you really want, so on, and burned away. How confident are you, right now, that you can do A to get B?

For instance, from this online resource on self-efficacy, comes a standard practice question, drived from Bandura’s famous “Guide to Creating Self-Efficacy Scales” (PDF).

If you were asked to lift a 10 pound object right now, how certain are you that you can lift it?

Respondants are given 11 choices, from 0 to 100, with 0 meaning cannot lift at all, and 100 meaning can lift without any problem.

When I first encountered self-efficacy I thought it was just a proxy of domain knowledge or long-term memory, but many, many studies show it is a seperate construct that explains variation on its own. A popularization of the concept is available from the Wall Street Jounal.

So now, I am planning to use self-efficacy, along with the rest of my model, to look at creativity in blogging. This is a large task and I need an organizational structure. Fortunately, Siwatu (2005) provides an excellent model. While Siwatu examined a different concept, we share a methodological outlook as well as a focus on self-efficacy.

So, using Siwatu as a model, how I plan to attack the problem. Italicized headings are areas where I replaced Siwatu’s topics with analogous ones in my own research.

Chapter 1 Introduction 1
Purpose of the Study 4
Research Questions 7
Definition of Terms 7
Blogging 7
Blogging Self-Efficacy 8
Blogging Creativity 8

Chapter II Review of the Literature 9
Creativity
Creation 11
The Novel 12
The Useful 14
The Field 16
The Domain 17
The Value of Creativity 17

Self-Efficacy
What are self-efficacy beliefs? 19
Source of Information 20
Mastery Experience 20
Vicarious Experience 20
Verbal Persuasion 21
Physiological and emotional states 21
Assessment of Self-Efficacy 22
The development of the CES Scale 25
Concerns regarding CES 29
What are Job and Creativity Self Efficacy? 33
Cognitive, Motivational, and Strategies Variables 35

General Ability 35
Domain Knowledge 37
Motivation 38
Strategies 41

Summary and Predictions 42
Chapter 3 Methods 46
Introduction 36
Quantitative Phase 36
Population and Sample 47
Measures 47
Creative Blogging Self-Efficacy Scale 47
Job Blogging Self-Efficacy Scale 48
Blogging Domain Knowledge Scale 49
Attitude Scale 50
General Ability Scale 51

Data Analysis 52

This model is missing a replacement for Siwatu’s qualitative section. I imagine that will come from creating and revising the scales I need in this research.

A Simple Model of Performance

Over the past year I have worked on grounding John Boyd’s OODA loop in modern psychology. Both here, and more often in academic drafts, I have described Orientation as System 1 or intuitive cognition, Decision as System 2 or deliberate cognition, Observation as perception and Action as behavior. I still think that the OODA model, or something very much like it, is probably the best high-level conceptual model of the human mind that we have available.

However, it does not help us understand what causes variance in the population, in most tasks. The reason for this is that it does not directly address the issue of Motivation. To use a computer metaphor if Orientation or System 1 is the hard drive, controller cards, and BIOS, while System 2 is RAM, then Motivation is the hypervisor, or that thing that controls the ability of everything else to engage in behaviors to achieve a goal. Motivation, or the hypervisor, is useful because it regulates System 1/Orientation/Long-Term Memory’s and System 2/Decision/Working Memory’s control of behavior, which in turn affects performance. A model of cognition that does not include performance misses both motivation’s regulation of behavior, and motivation’s direct impact on performance. A model derived from Horn et al. (1993) may give us a way forward:

The cognitive components of this model can each be broken down into sub-components. Long-term memory includes both procedural knowledge (how to ride a bike, how to tie your shoes) and declarative knowledge (how you would answer questions: what is a bike? what are shoes?). Many tasks require procedural and decalarative knowledge to operate together. Working-memory includes visual working memory, which is in tasks such as imagining the rotation of objects in three-dimensional space, and verbal working memory, which is used to remember lists, numbers or names. As far as I can tell, motivation loads from both self-efficacy, the believe that as of now you can perform specific tasks to reach a goal, and attitudes, especially the enjoyment of a thing (as it relates to consumption) and desire to block out the world (as it relates to production).

This leaves the question of where the Central Executive is. John Sweller has argueed that it exists in Long-Term Memory, and indeed that no central executive is conceivable other than one that operates through a darwinistic random process within System 1 / Orientation. Alan Baddeley asserts it is a third component of working memory, alongside visual and verbal working memory, because central executive functions appear to tax working memory capacity. Albert Bandura asserts that humans are “agents,” and their Central Executive agency must rely within their Motivation. I don’t know.

The harmonization of John Boyd’s OODA loop with this model of what actually predicts performance is an important task for the field of psychology, especially if it can account for creativity. I hope somebody does it.

Some Thoughts on Creative Self-Efficacy

In this post, I outline existing research on the role of self efficacy in creativity and propose future research to further explain the power of creative self-efficacy. To do this, I first discuss the concept of self-efficacy and its measurement, as described by Bandura (1977, 1997), as well as creativity, especially as discussed by (Abuhamdeh and Csikszentmihalyi 2002). I then clarify what is meant by creative self-efficacy, relying primarily on three studies: Tierney and Farmer’s (2002), Choi (2004), and Jaussi, Randel, & Dionne (2007). Lastly, I describe a study that might be done to explore it in the context of creativity, creative self-efficacy, and self-efficacy (Bandura, 2006).

In the first study, Tierney and Farmer (2002) examined how self-efficacy predicts creativity. Understanding creativity as the creation of the useful and the novel in a domain, Tierney and Farmer proposed that creativity in a domain should be predicted both by self-efficacy for that domain and self-efficacy for creativity. The authors proposed that job tenure, education level, job self-efficacy, supervisor support, job complexity, and job tenure would all positively predict creative self-efficacy. All of these hypotheses were confirmed in a study of 584 employees at large corporation except for the last: there was a negative correlation between creative self-efficacy and tenure, which is puzzling if creativity is the result of increased domain-specific learning. A follow up study of 191 workers at a research and development unit of a Midwest chemical company (Tierney & Farmer, 2004) found similar results, with creative self efficacy explaining 35% of employee creativity. In the second study, the correlation of creativity to task expertise was -.11, implying greater task expertise weakened creative performance.

The second study to be examined is Choi (2004). Choi proposed that a number of psychological mediators of creativity, including creative self-efficacy, creative intention, and creative personality, and to test this surveyed 430 students at a business school. Choi’s confirmatory analysis showed that creative self-efficacy explained 34% of the variance in creative performance, while creative intention explained 24%, and creative personality did not explain any additional variation, once other variables such as cautious personality were added to a longitudinal structural model.

The role of creative self-efficacy was further described by Jaussi, Randel, & Dionne (2007). Jaussi and colleagues conducted a treatment on 219 professional senior managers Creative self-efficacy was measured using the Tierney and Farmer (2002) scale, and creativity was measured through co-worker appraisal. Other variables, such as creative identity, stress at work, and gender were also gathered. The authors were primarily interested in the role of creative identity in predicting creativity, though also hypothesized that creative identity would interact with creative self-efficacy in predicting creativity. Hierarchical regression analysis continued to support the role of creative self-efficacy. Creative self-efficacy’s statistical significant impact was apparent after two steps of the analysis, while creative self-identity required another step to appear. However, the effect sizes were small, with all independent variables together only accounting for 14% of the variance in creativity.

Unfortunately, these studies suffer from methodological flaws which limit their generalizability. Consider how the studies treat creativity: Choi’s (2004) definition as “creativity as the generation of novel or original ideas that are useful or relevant” (p. 188), Tierney & Farmer’s (2002) definition as “the generation of domain-specific… novel, and useful outcomes” (p. 138), and Jaussi, Randel, & Dionne’s (2007) definition as “the production novel and useful ideas” (p. 247) are all close to each other, and to definitions of creativity used in other articles (Mayer, 1999). However, all of these paper use reports by instructors (Choi, 2004), a work supervisor (Tierner & Farmer, 2002, 2004), or co-workers (Jaussi, Randel, & Dionne, 2007). Thus, creativity is operationalized as the positive impression one makes on co-workers, rather than paying attention to a field, “all the individuals who act as gatekeepers to a domain,” that are the arbitrators of creativity (Abuhamdeh and Csikszentmihalyi, 2002, p. 37). Consider Abuhamdeh and Csikszentmihalyi’s example of the field in the domain of art: “the field consists of the art critics and art historians, the art dealers and art collectors, and the artists themselves.” Whether or not the field might also include the supervisors of the creative individual, it is clear a field would not be limited to them.

Methodological flaws also weaken the research on creative self-efficacy. Creative self-efficacy is an individual’s belief in his ability to perform a task in order to achieve a goal (Bandura, 1997, 2006). Efficacy varies in terms of the magnitude, generality, and strength of the expectation (Bandura 2006). Self-efficacy can come from an individual’s own accomplishments, observing a model, persuasion, or emotional arousal. The conceptual definitions are less clear, referring to “employees’ beliefs in their ability to be creative in their work” (Tierner & Farmer, 2002, p. 1141), “perceived behavioral control… in the context of creative performance. In other words… a person’s belief that he or she can successfully perform creative behavior in a particular setting” (Choi, 2004, p. 190), or “feelings about whether he or she is creative (feels confident that he or she can be creative in a given task)… the capacity to do a job creatively” (Jaussi, Randel, & Dionne, 2007, p. 249). Examples of the questions used to capture creative self-efficacy were “I have confidence in my ability to solve problems creatively” (Tierney & Farmer, 2002, p. 1141), a formulation also used in Tierney and Farmer (2004) and Jaussi, Randel, & Dionne (2007), as well as “I feel confident that I can introduce new ideas to the class in a convincing manner” and “I feel nervous when I present different views to classmates” (Choi, 2004, p. 139).

Self-efficacy should be defined and operationalized in a way keeping with the established method (Bandura, 2006). If the same construct if measured in different ways in different papers, the research on the topic will become confused. Indeed, as Bandura writes, “The construction of sound efficacy scales relies on a good conceptual analysis of the relevant domain of functioning” (p. 310). Without a good understanding of what it means to be creative, neither creativity nor creative self-efficacy can be measured!

Bandura begins his guide to operationalizaing self-efficacy with the following definition, originally from Bandura (1977): “Perceived self-efficacy is concerned with people’s beliefs in their capabilities to produce given attainments” (p. 307). While no standard self-efficacy scale exists, a standard way of generating them does (Bandura, 2006). Self-efficacy scales should be domain-specific, target factors that help predict proper functioning in the domain, should vary in the difficulty of the rated task so that individual differences in self-efficacy can be examined, and where motivation is a factor should measure self-efficacy to sustain the action. The definition and operationalization of self-efficacy is straight-forward (Bandura, 2006). To measure self-efficacy, questions “should be phrased in terms of can do rather than will do” (p. 308), Self-efficacy questions should ask about the confidence of the individual to perform a specific task as of now. Scales should include 11 steps, beginning at 0 and ending at 100. A practice item should be included to help people understand the scale. After scale construction, the scale should be pre-tested and the Cronbach’s alpha score should be used to determine which items to keep.

Abuhamdeh and Csikszentmihalyi (2002) present a guide for measuring creativity when they describe it as an individual who operates in a domain to gain recognition by the field. Thus, creativity can be measured by the recognition of the field. Interesting, the ELO system that is used by researchers to measure expertise (Charness, Krampe, & Mayr, 1996) is thus a valid measure of creativity. In ELO, every game that a chess player plays against another chess player is recorded. The histories of the players are used to judge the relative difficulty of the game for each. Thus, if a player with a better record defeats a player with a worse record little will change: that is expected. However, if a player with a substantially worse record upsets a player with a better record, it will lead to the previously worse player rising and the previously better player falling. In ELO, therefore, recognition by a domain is objective measured through a player’s recorded interaction with the field.

I propose to expand my prior research in the light of creative self-efficacy. Previously, I conducted a correlational pilot study on 77 blog readers and writers. I was interested in exploring the role of attitudes on creativity, so I measured cognitive and affective attitudes from a standard scale (Crites, et al., 1994), some questions relating to behaviors typical of creative people, and so on. I found that I could explain 20% of variation in creativity using three of the behavioral questions among blog creators, and I could explain 18% of the variation in consumption of blogs among readers by their affective attitude toward blogs. At the time, I was puzzled that affective attitudes. However, I decided that they might do to the concept of flow (Abuhamdeh and Csikszentmihalyi 2002), in which people participate in creative actions in order to avoid shut out the outside world. That is, it might be that blogging is not affectively agreeable, but that not-blogging would be affectively disagreeable. My readings on self-efficacy and creative self-efficacy make me believe that I might be able to explain a large amount of variance in creativity through a combination of withdrawal affect, practice, self-efficacy, and creative self-efficacy. Creativity combines both domain-specific components, such as purposeful practice in a domain, and domain-general components, including many psychological processes (Plucker & Beghetto, 1996). My study would help explain creative performance in terms of both domain-general creative self-efficacy and domain-specific self-efficacy.

References

Abuhamdeh, S. & Csikszentmihalyi,M. (2002). The artistic personality: a systems perspective, in R.J. Sternberg, E.L. Grigorenco, & J.L. Singer (eds) Creativity: from potential to realization. Washington, D.C. American Psychological Association.

Amabile, T.M. (1988). A model of creativity and innovation in organizations. In B.M. Staw & L.L. Cummings (Eds.), Research in organizational behavior, 10, 123-167. Greenwich, CT: JAI Press.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.). Self-efficacy beliefs of adolescents, 5, pp. 307-337. Greenwich, CT: Information Age Publishing.

Charness, N., Krampe, R., & Mayr, U. (1996). The role of practice and coaching in entrepreneurial skill domains: An international comparison of life-span chess skill acquisition. In The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games, (K. A. Ericsson, Ed., pp 55-80), Mahwah, NJ: Erlbaum.

Choi, J.N. (2004). Individual and contextual predictors of creative performance: The mediating role of psychological processes. Creativity Research Journal, 16(2 & 3), 187-199.

Crites, S.L., Jr., Fabrigar, L.R., & Petty, R.E. (1994). Measuring the affective and cognitive properties of attitudes: Conceptual and methodological issues. Personality & Social Psychology Bulletin, 20, 619-634.

Jaussi, K.S., Randel, A.E., & Dionne, S.D. (2007). I am, I think I can, I do: The role of personal identity, self-efficacy, and cross-application of experiences in creativity at work. Creativity Research Journal, 19(2 & 3, 247-258).

Mayer, R.E. (1999). Fifty yeras of creativity research. In R.J. Sternberg (Ed.), Handbook of creativity (pp. 449-460). New York: Cambridge University Press.

Plucker, J.A. & Beghetto, R.A. (1996). Why creativity is domain general, why it looks domain specific, and why the distinction does not matter. In The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games, (K. A. Ericsson, Ed., pp. 153-167), Mahwah, NJ: Erlbaum.

Tierney, P.A. & Farmer, S.M. (2002). Creative self-efficacy: its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137-1148.

Tierney, P.A. & Farmer, S.M. (2004). The Pygmalion process and employee creativity. Journal of Management, 30(3), 413-432.

The OODA Loop and Creativity, Try 2

Afer heavily revising my original work tying together the OODA loop to creativity, it has become clear that the project will involve much more than a few references in another paper. So I’m removing it from my current draft, but (for posterity) here were my final thoughts on Creativity in the context of OODA, at least before my work on the taxonomies of creativity changed my perspective.


The OODA loop was originally described to assist in understanding the nature of creativity (Coram, 2002). Creative thoughts, according to Boyd (1992), “permit us to rematch our mental/physical orientation [with the outside world] and grow” in the world (24). Before individuals can be creative, they must be experts. This requires reorientation. However, reorientation that leads to creativity is difficult because expertise requires considerable practice outside of an educational setting. Fortunately, educational psychology’s initial work on dual processing presents a way forward.
Modern research views creativity as a subset of expertise. This formulation is expanded on at length in Herbert Simon’s exploration of creativity in the arts and sciences . Expertise is defined by strong problem solving ability in a particular field, meaning quick scanning of a problem space for any of a large number of remembered patterns (Simon, 2001). Expertise tends to require more than ten years to develop, and a narrowness of focus that can involve both personal and intellectual sacrifices including slowed or reduced growth in other fields that are not focused on or practiced (Simonton, 2003).
The importance of reorientation in creativity is stressed by a recent overview of the growing literature on cognitive load. For instance, van Merrienboer & Sweller (2005) write that “expertise comes from knowledge stored in [long term memory] schemata, not from an ability to engage in reasoning with many elements that have not been organized in long-term memory. Human working memory simply is not able to process many elements” (149-150). Similarly, Sweller (2004) describes how random variations in ideas are necessary for creative products to be new, and not mere memories. The product of such thinking is “change via novelty” (20). Likewise, cognitive load describes originality is seen as random variations in the expression of long-term memory, and original ideas are kept or lost depending on their fitness in the landscape of thought (Sweller, 2004; van Merrienboer & Sweller, 2005). Yet as randomness in thought is assumed to be constant among all individuals, what makes those new ideas more likely to be useful is whether they are grounded in an orientation rich in relevant long term memory.
The scale of the difficult in encouraging creativity is gleaned from a recent discussion on minimal guidance instruction (Kirschner, Sweller, & Clark, 2006). Directly addressing the question of discovery-based styles of instruction, Kirschner and colleagues distinguish expert from novice operating styles. Summarizing research on the expert/novice divide, the authors conclude that attempting to become an expert by replicating the style of experts is not a productive strategy. Yet in an increasingly complex world where many learners will go on to perform tasks that do not currently exist, some form of discovery-based learning will be necessary for the development of expertise in these fields of the future. The task of the educator is not enviable.
Fortunately, educational psychology’s early approaches to dual processing show the way forward. Motivation and good attitude, focused on by Sinatra (2005) and Gregorie (2005), provide an approach that will encourage learners to continue their own reorientation long after they leave the classroom. To put it simply, motivation leads to more practice, more practice loads to more expertise, and more expertise leads to more creativity. In creativity as with other fields of educational psychology, dual processing and OODA do not overturn what we already know. Rather, the Observe-Orient-Decide-Act dual-processing cycle enrich what educational psychology has already discovered about providing the best education possible to our learners.

Boyd, J.R. (1992). The conceptual spiral. Retrieved October 1, 2007, from Belisarius. Web site: http://www.d-n-i.net/boyd/pdf/intro.pdf.
Coram, R. (2002). Boyd: The fighter pilot who changed the art of war. New York: Back Bay Books.
Gregorie, M. (2003). Is it a challenge or a threat? A dual-process model of teachers’ cognition and appraisal process during conceptual change. Educational Psychology Review, 15(2), 147-179.
Kirschner, P.A., Sweller, J., Clark, R.E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86.
Simon, H.A. (2001). Creativity in the arts and sciences. In Cultures of creativity: The centennial celebrations of the Nobel Prizes. Kenyon Review, Spring, 23(2), 203-220.
Simonton, D.K. Expertise, competence, and creativity ability: The perplexing complexities. (2003). In The Psychology of Abilities, Competencies, and Expertise (R.J. Sternberg & E. Grigorenko, Eds.) New York: Cambridge University Press.
Sinatra, G.M. (2005). The “warming trend” in conceptual change research: The legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 107-115.
Sweller, J. (2004). Instructional Design Consequences of an Analogy between Evolution by Natural Selection and Human Cognitive Architecture. Instructional Science, 32(1/2), 9-31.
van Merrienboer, J.J.G. & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.

Taxonomies of Creativity

After Mark posted his thoughts, I recently completed two books on creativity, talent, and expertise: The Road to Excellence: The Acquistion of Expert Performance in the Arts and Sciences, edited by K. Anders Ericsson, and Creativity: From Potential to Realization, edited by Robert J. Sternberg Elana L. Grigorenko, and Jerome L. Singer. From the chapters in the book, it seems reasonable to divide the study of “creativity” to the study of talent, creativity, expertise, and invention. That is

  • Talent is the potential for Creativity, Expertise, and Invention
  • Creativity = Talent + training
  • Expertise = Creativity+ overtraining
  • Invention = Creativity + profitmotive

Visually:

What is creativity?

Identifying expertise as a subset of creativity, rather than the reverse, is not something I’ve seen before. But I think it’s valid. Both rely on a high degree of domain-specific knowledge. The difference appears to be that mere Experts are foreclosed to creativity by over rigid mental structures, ignoring conflicting observations, lack of psychopathology, and other things that can be avoided by the looser (but still knowledge-rich) thinking of the creativity.

Beyond this, my other notes are more prosaic and deal with the creativity research itself. Such research is correlational, biographical clinical/on-site, laboratory, or computational. It studies domains such as academics, arts, sports, or professions. It follows the research agenda of cognitivism, social cognitivism, developmentalism, complex dynamic systems. Most researchers view creativity as domain-specific, though some argue it is domain-general.

Doing Artsy Stuff Isn’t “Creativity”

I’ve talked about creativity before, in the context of the OODA loop, purposeful practice (a form of metacognition that is the opposite of “flow”), and mental illness. Another part of creativity is being recognized as useful by the field of a domain. If you invent a new type of hot water heater, that is being creative. If you’re chess technique allows you to rise in international chess competitions, that’s creativity. If you cure cancer but don’t tell anyone, that’s just wasting your time.

So this article is somewhat off-base:

Why Do Men Share Their Creative Work Online More Than Women? | Scientific Blogging
A recent Northwestern University study has a surprising results – substantially more men are likely to share their creative work online than women even though both genders engage in creative activities at essentially equal rates.

As it confuses artsy-stuff (making music, taking photographs, etc.) with creativity. Certainly artsy-stuff can be a form of practice, therapy, or good old recreation. Perhaps it can lead to creativity one day when you share it with others. But if you sit on it, you’re enjoying yourself, not being creative.

What is creativity?

Half Sigma ponders in some detail what it means to be creative, and he gives some factors associated with that label. While HS doesn’t go into everything, according to respectable lines of psychological research is predicted by

  • Background knowledge of the domain of the creative work
  • Working memory capacity
  • Self efficacy in the domain
  • Creative self efficacy
  • Openness to new experiences
  • Psychosis

Psychosis in this context can be thought of as “horizontal thinking,” “distant relationships, etc.” The appropriate level of psychosis varies depending on the domain. So clinical levels of psychosis is much higher among creative modern artists than among creative thermodynamic physicists, because domains with strong degrees of standardization weed out psychotics before they have the appropriate background knowledge needed to be creative in the first place.

The ordering of the “risk factors for creativity” is still open to debate. For instance, someone with low self efficacy in a domain, low working memory, and low openness to new experience can be just as creative, if they are forced to learn the materials required to get them background knowledge in the domain of the creative work.

Multiple Inventions, Multiple Evolutions

My friend Jayson has emailed me a New Yorker article, “In the Air” by Malcom Gladwell. I previously saw it referenced by Peter Robinson over at The Corner, so it has now caught my eye twice.

The article talks about multiple discovery, where independent researchers or teams invent the same thing at about the same time — but have little to do with each other. The famous example is calculus for which (after being unknown for all of human history) Isaac Newton and Gottfiend Leibniz created systems so similar that their main difference was the philosophical question of whether the super-small things changing were “infinitesimals” or “fluxions.”

In truth, most discoveries are probably multiple discoveries.

Consider two papers, one an article and the other a blog post, which help explain the issues John Sweller’s peer-reviewed article, Instructional Design Consequences of an Analogy between Evolution by Natural Selection and Human Cognitive Architecture,” published in the January 2004 edition of Instructional Science,” describes how thinking relies on stored information in long-term memory, and how the random errors that happen during remembering provide opportunities for feedback, “good mistakes” are rewarded while bad mistakes are discarded. Similarly, Razib’s primer, “8th grade math for the rest of us,” published November 2005 at gnxp, describes the probability of an gene that is helpful actually becoming common in a population.

Essentially, chance operates two ways in evolution: introducing new versions of things by chance, and rewarding or discarding new versions by chance. In order to become common, a new thing both most be lucky enough to be crated, and lucky enough to spread. (Even if a mutation is helpful, for example, if the animal carrying it is struck by lightning, it’s gone.)

The same is true of inventions. Chance operates two ways: for the invention to be created, or the invention to be accepted. Just because an inventor is inspired to build a new product that works, and works better than anything else at its job, doesn’t mean that the creator will be able to convince other people that it’s worth while, etc.

Of course, sometimes multiple versions of an invention become known. This has happened with skin color: East Asian and European “whiteness” derive from different mutations. This has also happened with calculus: Newton’s and Liebenz’s systems derive from different assumptions. But in both cases, the need for the invention was there, the tools needed to create the invention was there: all it needed to do was happen.

Of course, there may have been a third version of calculus, created in about the same circumstances, that is now lost and forgotten. Likewise, there may have been another evolutionary fork for creating light skin that is lost.

All of this has important implications for intellectual property law. But that is a post for another time…

Please take a survey… for science!

Title: Public Request for Participation

This is a public request for participation for an academic project, “Creativity and Blogging .”

This project attempts to discover what is the relationship between blogging success and attitude. If you choose to participate, you will take a survey aimed at discovering what you think about blogging, what you feel about blogging, and what you do about blogging. You would also be asked a few questions regarding your personality and your views on cooperation. This involves answering about seventy questions. The survey should take about 30 minutes.

You will receive no direct benefit from participating. The only indirect benefit you would receive is the knowledge you are assisting in ongoing scientific research. No compensation is provided Before you begin the survey, you will be shown an informed consent form with additional details. Of course, you can stop taking the survey at any time.

If you agree to participate, please follow the below link to take the web survey:

Take a survey on Creativity and Blogging

Thank you,

The Creativity and Blogging Team

Creativity

Adding to the swarm of blogtalk about innovation (MountainRunner, the first post at ERM, this blog, Zenpundit once, and Zenpundit twice), Stephen DeAngelis comes to the topic of creativity. He gives some numbered myths about creativity

  1. there is always a “eureka” moment
  2. there is a clear path to innovation
  3. people “dig” new ideas
  4. the lone innovator
  5. most people can’t be creative
  6. you’ll know innovation when you see it
  7. the best ideas wins
  8. innovation is always good

Those interested in creativity may be interested in my analysis of Coming Anarchy, where I identified several factors necessary for creative success, including:

Finally, I also interviewed Steve’s co-worker, Tom Barnett, as a project for the same doctoral class on creativity that generated the CA analysis.