Citation
R. Sun and I. Naveh, Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, Vol.7, No.3. 2004.
Summary / Assessment
In their paper, Sun and Naveh argue that social simulation needs cognitive science concepts. Social interaction is basically the result of individual cognition; therefore, the more accurately one can model individual agents and their cognitive processes, the more accurate and dependable the social model will become. The authors also argue that utilizing a realistic cognitive model helps us to better understand individual agent cognition, more specifically the socio-cultural aspects of cognition and how individual agents learn from each another. The paper mentions how the CLARION cognitive architecture is well suited to model cognitively accurate agents within a social simulation. The authors describe CLARION’s dual representation of human learning, and go into detail on how the lower layer (the implicit layer) interacts with the upper layer (the explicit layer). They cite this dual-representation as being fundamental in constructing realistic cognitive agents.
The paper moves on to explain an organizational task that is to be studied. The task is to determine if a blip on a radar screen is a hostile aircraft, a flock of geese, or a civilian aircraft (i.e. – whether the blip is an enemy, neutral, or friendly). Each blip has nine attributes that carry a weighted value. If the sum of the attributes is less than 17 it is friendly, if greater than 19 it is hostile, otherwise it is neutral.
The organization is presented with 100 problems (100 blips). An organization’s performance is a measure of how accurately it can classify blips.
Originations are set up in one of two ways, as a team structure or as a hierarchy. In a team structure, every agent’s calculations are equally weighted and the final result is determined democratically. In a hierarchy, agents’ decisions are passed on to a supervisor agent, and the supervisor makes the final decision. Additionally, information is disbursed within the organization in one of two ways. In a distributed fashion where no single agent has access to all required pieces of information, or in a blocked fashion where each agent has access to all pieces of required information. (In both cases, all pieces of information are accessible to the agents as whole.)
Earlier studies, performed by Carley et al in 1998, revealed that humans typically perform better in a team situation, especially when information was provided in a distributed manner (no single agent has access to all pieces of required information). The worst performance occurred in hierarchical team structures that used blocked data access (every agent has access to all pieces of required information). In the same study, Carley used four cognitive architectures, other than CLARION, to simulate the same task. Results from these simulations were compared with human data. The comparison showed that the simulations did not provide results that were inline with human capabilities. It is argued that this is due to shortcomings in the architectures to accurately model human intelligence and learning.
Sun and Naveh simulated the same task with the CLARION cognitive architecture. Results from this simulation were compared with the actual human data and it was found that CLARION was able to provide results that were inline with human results. It is argued that the similarity is due to the fact that CLARION accurately models human learning in its dual representation structure. The authors then augmented the simulation by extending runtime, by varying cognitive parameters (such as learning rate, and temperature of randomness), and by introducing differences in the cognitive agents (such as introducing weak learners). The overall result is that when using CLARION, one can create cognitively realistic social simulations yielding results that are in accord with psychological literature.
Saturday, April 23, 2011
Friday, April 8, 2011
A Summary of "Collective Intelligence: Mankind’s Emerging World in Cyberspace"
Citation
Levy, Pierre. "Introduction." Collective Intelligence: Mankind’s Emerging World in Cyberspace. Cambridge, Massachusetts: Perseus Books, 1999. 1-19.
Summary / Assessment
In the introduction to his book, Levy first discusses collective intelligence as it relates to economy. He writes: “the prosperity of a nation, geographical region, business, or individual depends on their ability to navigate the knowledge space.” The more we can form intelligent, highly capable communities, the more we can ensure our success in a highly competitive environment. As an example: businesses transform themselves to promote information exchange between departments. This results into what Levy calls “innovation networks”. Departments can easily interact with one another transferring knowledge, personnel, and skills. This allows companies to be more receptive to ever changing demand for skills (such as scientific, technical, social, and aesthetic skills). An organization that is inflexible to changing skills, will eventually collapse.
Levy defines an anthropological space as: “a system of proximity (space) unique to the world of humanity (anthropological), and thus dependant on human technologies, significations, language, culture conventions, representations, and emotions”. Four spaces are defined: earth, territorial space, commodity space, and knowledge space. The Earth Space defines our identity in terms of our “bond with the cosmos”, as well as our affiliation or alliance with other humans (ex: our name is a symbol representing out place in an ancestral line). In the Territorial Space, the meaning of identity shifts. Identity, in this space, is linked with the ownership of property and group affiliations (ex: our home address identifies our geographic location as well as affiliations with certain groups of individuals (our neighborhood)). In the Commodity Space, identity is defined by one’s participation in the process of moving commodities (goods). This includes involvement in the production of goods and involvement in the exchange of goods. In the fourth space, the Knowledge Space, one’s identity is defined by knowledge and the capacity to rapidly acquire knowledge. Levy identifies three aspects of the Knowledge Space: Speed – information/knowledge can rapidly be acquired. Mass – It is impossible to restrict the movement of knowledge; therefore a larger mass of individuals now has access to information/knowledge. Tools – tools have been created that enable individuals to acquire, manage, and filter information as needed.
Levy defines collective intelligence as “a form of universally distributed intelligence, constantly enhanced, coordinated in real-time, and resulting in the effective mobilization of skills.” Knowledge is enhanced at the level of the individual as we try to better ourselves through the acquisition of new skills and abilities. Intelligence is coordinated in real-time through digital mediums and emergent technologies. Skills are mobilized first by acknowledging an individual’s skills, and then by recognizing the individual’s contributions to the collective.
Levy also adds to his definition of collective intelligence by writing, “the basis and goal of collective intelligence is the mutual recognition and enrichment of individuals, rather than the cult of fetishized or hypostatized communities”. In other words, the goal of collective intelligence should be that individuals are compensated or acknowledged based on the quantity and more importantly, the quality of their contribution to the collective. Furthermore, acknowledgement should be made at the level of the individual, rather than at the level of the collective.
Levy, Pierre. "Introduction." Collective Intelligence: Mankind’s Emerging World in Cyberspace. Cambridge, Massachusetts: Perseus Books, 1999. 1-19.
Summary / Assessment
In the introduction to his book, Levy first discusses collective intelligence as it relates to economy. He writes: “the prosperity of a nation, geographical region, business, or individual depends on their ability to navigate the knowledge space.” The more we can form intelligent, highly capable communities, the more we can ensure our success in a highly competitive environment. As an example: businesses transform themselves to promote information exchange between departments. This results into what Levy calls “innovation networks”. Departments can easily interact with one another transferring knowledge, personnel, and skills. This allows companies to be more receptive to ever changing demand for skills (such as scientific, technical, social, and aesthetic skills). An organization that is inflexible to changing skills, will eventually collapse.
Levy defines an anthropological space as: “a system of proximity (space) unique to the world of humanity (anthropological), and thus dependant on human technologies, significations, language, culture conventions, representations, and emotions”. Four spaces are defined: earth, territorial space, commodity space, and knowledge space. The Earth Space defines our identity in terms of our “bond with the cosmos”, as well as our affiliation or alliance with other humans (ex: our name is a symbol representing out place in an ancestral line). In the Territorial Space, the meaning of identity shifts. Identity, in this space, is linked with the ownership of property and group affiliations (ex: our home address identifies our geographic location as well as affiliations with certain groups of individuals (our neighborhood)). In the Commodity Space, identity is defined by one’s participation in the process of moving commodities (goods). This includes involvement in the production of goods and involvement in the exchange of goods. In the fourth space, the Knowledge Space, one’s identity is defined by knowledge and the capacity to rapidly acquire knowledge. Levy identifies three aspects of the Knowledge Space: Speed – information/knowledge can rapidly be acquired. Mass – It is impossible to restrict the movement of knowledge; therefore a larger mass of individuals now has access to information/knowledge. Tools – tools have been created that enable individuals to acquire, manage, and filter information as needed.
Levy defines collective intelligence as “a form of universally distributed intelligence, constantly enhanced, coordinated in real-time, and resulting in the effective mobilization of skills.” Knowledge is enhanced at the level of the individual as we try to better ourselves through the acquisition of new skills and abilities. Intelligence is coordinated in real-time through digital mediums and emergent technologies. Skills are mobilized first by acknowledging an individual’s skills, and then by recognizing the individual’s contributions to the collective.
Levy also adds to his definition of collective intelligence by writing, “the basis and goal of collective intelligence is the mutual recognition and enrichment of individuals, rather than the cult of fetishized or hypostatized communities”. In other words, the goal of collective intelligence should be that individuals are compensated or acknowledged based on the quantity and more importantly, the quality of their contribution to the collective. Furthermore, acknowledgement should be made at the level of the individual, rather than at the level of the collective.
Tuesday, March 29, 2011
A Summary of "The Motivational and Meta-Cognitive Subsystems" from "A Tutorial on CLARION 5.0"
Citation
Sun, Ron. "Chapter 4: The Motivational and Meta-Cognitive Subsystems." "A Tutorial on CLARION 5.0." Department of Cognitive Science. Rensselaer Polytechnic Institute. 6 Oct. 2009. http://www.sts.rpi.edu/~rsun/sun.tutorial.pdf
Summary / Assessment
In this chapter, the Motivational Subsystem (MS) and the Meta-Cognitive Subsystem (MCS) of the CLARION architecture are described. The MS is concerned with an agent’s drives and their interactions (i.e. – why an agent does what it does and why it chooses any particular action over another). The MCS controls and regulates cognitive processes. The MCS accomplishes this, for example, by setting goals for the agent and by managing ongoing processes of learning and interactions with the surrounding environment.
Dr. Sun mentions that motivational and meta-level processes are required for an agent to meet the following criteria when performing actions: sustainability, purposefulness, focus, and adaptivity. Sustainability refers to an agent attending to basic needs for survival (i.e. – hunger, thirst, and avoiding danger). Purposefulness refers to an agent selecting activities that will accomplish goals, as opposed to selecting activities completely randomly. Focus refers to an agent’s need to focus its activities on fulfilling a specific purpose. Adaptivity refers to the need of an agent to adapt (i.e. – to learn) to improve its sustainability, purposefulness, and focus.
When modeling a cognitive agent, it is important to include the following considerations concerning drives. Proportional Activation: Activation of drives should be proportional to offsets or deficits within the agent (such as the degree of the lack of nourishment). Opportunism: Opportunities must be factored in when choosing between alternative actions (ex: availability of water may lead an agent to choose drinking water over gathering food, provided that the food deficit is not too high). Contiguity of Actions: A tendency to continue the current action sequence to avoid the overhead of switching to a different action sequence (i.e. – avoid “thrashing”). Persistence: Actions to satisfy a drive should persist beyond minimum satisfaction. Interruption When Necessary: Actions for a higher priority drive should be interrupted when a more urgent drive arises. Combination of Preferences: Preferences resulting from different drives could be combined to generate a higher-order preference. Performing a “compromise candidate” action may not be the best for any single drive, but is best in terms of the combined preference.
Specific drives are then discussed. Drives are segmented into three categories: Low-Level Drives, High-Level Drives, and Derived Secondary Drives. Low-Level drives include physiological needs such as: get-food, get-water, avoid-danger, get-sleep, and reproduce. Low-Level drives also include “saturation drives” such as: avoid-water-saturation, and avoid-food-saturation. High-Level drives include “needs” such as: belongingness, esteem, and self-actualization (and others from Maslow’s needs hierarchy). Derived Secondary Drives include: gradually acquired drives through conditioning (i.e. – associating a secondary goal to a primary drive), and externally set drives (i.e. – drives resulting from the desire to please superiors in a work environment).
Meta-cognition refers to one’s knowledge of one’s own cognitive process. It also refers to the monitoring and orchestration of cognitive processes in the service of some concrete goal or objective. These concepts are operationalized within CLARION’s MCS through the following processes: 1) Behavioral Aims: which set goals and their reinforcements, 2) Information Filtering: which determines the selection of input values from the environment, 3) Information Acquisition: which selects learning methods, 4) Information Utilization: which refers to reasoning, 5) Outcome Selection: or determining the appropriate outputs, 6) Cognitive Modes: or the selection of explicit processing, implicit processing, or combination thereof, and 7)Parameter Settings: such as parameters for learning capability (i.e. – intelligence level).
Sun, Ron. "Chapter 4: The Motivational and Meta-Cognitive Subsystems." "A Tutorial on CLARION 5.0." Department of Cognitive Science. Rensselaer Polytechnic Institute. 6 Oct. 2009. http://www.sts.rpi.edu/~rsun/sun.tutorial.pdf
Summary / Assessment
In this chapter, the Motivational Subsystem (MS) and the Meta-Cognitive Subsystem (MCS) of the CLARION architecture are described. The MS is concerned with an agent’s drives and their interactions (i.e. – why an agent does what it does and why it chooses any particular action over another). The MCS controls and regulates cognitive processes. The MCS accomplishes this, for example, by setting goals for the agent and by managing ongoing processes of learning and interactions with the surrounding environment.
Dr. Sun mentions that motivational and meta-level processes are required for an agent to meet the following criteria when performing actions: sustainability, purposefulness, focus, and adaptivity. Sustainability refers to an agent attending to basic needs for survival (i.e. – hunger, thirst, and avoiding danger). Purposefulness refers to an agent selecting activities that will accomplish goals, as opposed to selecting activities completely randomly. Focus refers to an agent’s need to focus its activities on fulfilling a specific purpose. Adaptivity refers to the need of an agent to adapt (i.e. – to learn) to improve its sustainability, purposefulness, and focus.
When modeling a cognitive agent, it is important to include the following considerations concerning drives. Proportional Activation: Activation of drives should be proportional to offsets or deficits within the agent (such as the degree of the lack of nourishment). Opportunism: Opportunities must be factored in when choosing between alternative actions (ex: availability of water may lead an agent to choose drinking water over gathering food, provided that the food deficit is not too high). Contiguity of Actions: A tendency to continue the current action sequence to avoid the overhead of switching to a different action sequence (i.e. – avoid “thrashing”). Persistence: Actions to satisfy a drive should persist beyond minimum satisfaction. Interruption When Necessary: Actions for a higher priority drive should be interrupted when a more urgent drive arises. Combination of Preferences: Preferences resulting from different drives could be combined to generate a higher-order preference. Performing a “compromise candidate” action may not be the best for any single drive, but is best in terms of the combined preference.
Specific drives are then discussed. Drives are segmented into three categories: Low-Level Drives, High-Level Drives, and Derived Secondary Drives. Low-Level drives include physiological needs such as: get-food, get-water, avoid-danger, get-sleep, and reproduce. Low-Level drives also include “saturation drives” such as: avoid-water-saturation, and avoid-food-saturation. High-Level drives include “needs” such as: belongingness, esteem, and self-actualization (and others from Maslow’s needs hierarchy). Derived Secondary Drives include: gradually acquired drives through conditioning (i.e. – associating a secondary goal to a primary drive), and externally set drives (i.e. – drives resulting from the desire to please superiors in a work environment).
Meta-cognition refers to one’s knowledge of one’s own cognitive process. It also refers to the monitoring and orchestration of cognitive processes in the service of some concrete goal or objective. These concepts are operationalized within CLARION’s MCS through the following processes: 1) Behavioral Aims: which set goals and their reinforcements, 2) Information Filtering: which determines the selection of input values from the environment, 3) Information Acquisition: which selects learning methods, 4) Information Utilization: which refers to reasoning, 5) Outcome Selection: or determining the appropriate outputs, 6) Cognitive Modes: or the selection of explicit processing, implicit processing, or combination thereof, and 7)Parameter Settings: such as parameters for learning capability (i.e. – intelligence level).
Wednesday, March 9, 2011
A Summary of "The Key Characteristics of Produsage" from "Blogs, Wikipedia, Second Life, and Beyond - From Production to Produsage"
Citation
Bruns, Axel. "The Key Characteristics of Produsage." Blogs, Wikipedia, Second Life, and Beyond - From Production to Produsage. New York, NY.: Peter Lang Publishing, Inc, 2008-2009. 9-36.
Summary / Assessment
In the second chapter of “Blogs, Wikipedia, Second Life, and Beyond”, Axel Bruns discusses the idea of Produsage. He begins the chapter by giving background information on the traditional model of production. The traditional model contains three clearly defined, separate tasks: the producer, the distributor, and the consumer. One side effect of the traditional model is consumers are not active in product development. Highly competitive producers shield product development thereby making consumer involvement impossible. The traditional model gradually changed to involve consumers in a limited manner through focus groups and general market research. The consumer became more than just an end user of fixed products. Through feedback, the consumer was able to alter products based on his/her needs. (The term “prosumer” is used to describe such consumers.)
Bruns then discusses how the Internet shifts information consumption to information usage, and how it challenges the traditional stance on information production. He demonstrates this shift through the following points: With the internet, information access is on a pull-basis, rather than a traditional push-basis. Access to producing and distributing information is readily available and is not as limited as it once was. With technology, users can communicate and engage directly with one another, bypassing the traditional model. Digital information can be shared quickly and can be remixed to create new artifacts.
By means of the Internet, producers are able to move from the traditional hierarchical model to the distributed and communal network model. The term “hive-mind” is used to describe such a model. Here, users are intercreative and there is a “collective intelligence” that emerges. Four attributes of the collective model are identified: 1) Problem solving is probabilistic, not directed. Users can take a more holistic view of the system, leading to contributions in areas outside of those they may have been bounded to within a more traditional approach. 2) Equipotentiallity, not hierarchy. Equipotentiallity is the assumption that each participant can make constructive contributions to the system. 3) Granular tasks. Tasks should not be too complex to require significant administrative overhead. The size of each task must support probabilistic problem solving and Equipotentiallity of contributors. 4) Content is shared, not owned. Sharing content is fundamental to collaboration and supports the three previous points.
With a communal/network model, the idea of Produsage is possible. The cogently terse definition of Produsage given by the author is “the collaborative and continuous building and extending of existing content in the pursuit of further improvement.” The four principles of Produsage are: 1) Open Participation, Communal Evaluation. The basic idea here is that all users are able to participate in achieving the goal, and the more users participating, the more probability there is of identifying the most appropriate/correct solution. 2) Fluid Hierarchy, AD Hoc Meritocracy. “Ad-hocracies” are fluidly built based on the ideas of Equipotentiallity and communal evaluation. Users who actively contribute relevant material to the group have a higher standing, conversely, the user’s standing within the group can diminish if the quality of their contributions decline. 3) Unfinished Artifacts, Continuing process. The idea is that Produsage does not work towards the completion of products, but rather through iteration, creates successfully better products (i.e. - artifacts). 4) Common Property, Individual Rewards. Instead of focusing on monetary rewards, participation in Produsage is motivated by contributing to a communal purpose. These non-monetary rewards grow a community by encouraging individuals to make continual contributions to the overall goal.
Bruns, Axel. "The Key Characteristics of Produsage." Blogs, Wikipedia, Second Life, and Beyond - From Production to Produsage. New York, NY.: Peter Lang Publishing, Inc, 2008-2009. 9-36.
Summary / Assessment
In the second chapter of “Blogs, Wikipedia, Second Life, and Beyond”, Axel Bruns discusses the idea of Produsage. He begins the chapter by giving background information on the traditional model of production. The traditional model contains three clearly defined, separate tasks: the producer, the distributor, and the consumer. One side effect of the traditional model is consumers are not active in product development. Highly competitive producers shield product development thereby making consumer involvement impossible. The traditional model gradually changed to involve consumers in a limited manner through focus groups and general market research. The consumer became more than just an end user of fixed products. Through feedback, the consumer was able to alter products based on his/her needs. (The term “prosumer” is used to describe such consumers.)
Bruns then discusses how the Internet shifts information consumption to information usage, and how it challenges the traditional stance on information production. He demonstrates this shift through the following points: With the internet, information access is on a pull-basis, rather than a traditional push-basis. Access to producing and distributing information is readily available and is not as limited as it once was. With technology, users can communicate and engage directly with one another, bypassing the traditional model. Digital information can be shared quickly and can be remixed to create new artifacts.
By means of the Internet, producers are able to move from the traditional hierarchical model to the distributed and communal network model. The term “hive-mind” is used to describe such a model. Here, users are intercreative and there is a “collective intelligence” that emerges. Four attributes of the collective model are identified: 1) Problem solving is probabilistic, not directed. Users can take a more holistic view of the system, leading to contributions in areas outside of those they may have been bounded to within a more traditional approach. 2) Equipotentiallity, not hierarchy. Equipotentiallity is the assumption that each participant can make constructive contributions to the system. 3) Granular tasks. Tasks should not be too complex to require significant administrative overhead. The size of each task must support probabilistic problem solving and Equipotentiallity of contributors. 4) Content is shared, not owned. Sharing content is fundamental to collaboration and supports the three previous points.
With a communal/network model, the idea of Produsage is possible. The cogently terse definition of Produsage given by the author is “the collaborative and continuous building and extending of existing content in the pursuit of further improvement.” The four principles of Produsage are: 1) Open Participation, Communal Evaluation. The basic idea here is that all users are able to participate in achieving the goal, and the more users participating, the more probability there is of identifying the most appropriate/correct solution. 2) Fluid Hierarchy, AD Hoc Meritocracy. “Ad-hocracies” are fluidly built based on the ideas of Equipotentiallity and communal evaluation. Users who actively contribute relevant material to the group have a higher standing, conversely, the user’s standing within the group can diminish if the quality of their contributions decline. 3) Unfinished Artifacts, Continuing process. The idea is that Produsage does not work towards the completion of products, but rather through iteration, creates successfully better products (i.e. - artifacts). 4) Common Property, Individual Rewards. Instead of focusing on monetary rewards, participation in Produsage is motivated by contributing to a communal purpose. These non-monetary rewards grow a community by encouraging individuals to make continual contributions to the overall goal.
Thursday, February 24, 2011
A Summary of "Understanding How Consumers Make Decisions: Using Cognitive Task Analysis for Market Research" from "Working Minds"
Citation
Crandall, Beth; Klein, Gary; Hoffman, Robert R. "Understanding How Consumers Make Decisions: Using Cognitive Task Analysis for Market Research." Working Minds: A Practitioner’s Guide to Cognitive Task Analysis. Cambridge, Mass.: MIT Press, 2006. 215-228.
Summary / Assessment
In this chapter of Working Minds, the authors demonstrate how the application of cognitive task analysis (CTA) techniques to market research can help clarify various cognitive questions such as: “How do consumers decide whether to purchase a product?” “How do consumers make sense of what the product does or how it works?” -and- “When consumers have developed incorrect or inadequate mental models, what would help them shift to better models so they are more satisfied with the product?” The use of CTA techniques augments the traditional approach of doing market research by enabling us to discover the way consumers think about using and buying products, and how consumers make decisions. (The traditional approach is relegated to studying consumer beliefs, attitudes, and preferences.)
The traditional way of performing market research is to ask consumers how they make their purchasing decisions. This technique is incomplete because consumers are sometimes unable to explicitly express their cognitive processes when choosing a particular product. We must ask the appropriate questions and observe the consumer “in the wild” to infer their decision strategy. CTA techniques also provide a means to elicit and document the internal mental models consumers build about products. Their mental models include how they perceive the product (behaviorally, emotionally, and reflectively), and what makes the product effective or ineffective. In learning about product effectiveness, we also learn about how the product is being used and in what context it is being used.
The authors define the following three basic strategies for applying CTA methods to market research: Concurrent Observations and Interviews, Simulations and Props, and Retrospective Interviews. Concurrent Observations and Interviews are used while the consumers are making product decisions and while the consumers are interacting with products. They are used to record what users are thinking about in situ (not to record remembrances or hypothetical cases). Simulations and Props are used when it isn’t possible to observe consumers in action. We can use this technique to “probe consumer cognition”, to see what consumers were picking up on, and what they were considering or ignoring. Retrospective Interviews can be leveraged when trying to understand reflective issues such as brand loyalty and what prompts users to switch brands. (For example, the consumer’s history with the product is identified and examined in such interviews.)
Just as it is difficult for Consumers to express their cognitive processes when choosing or using a particular product, Users have difficulties when expressing the complex cognitive processes involved in their profession. Herein lies the nexus between studying consumer behavior and designing effective software products. Users may express what they think they need (new technologies, certain design scheme, etc.), but in reality it is hard for users to express -or translate- their true needs into exact specifications. Professionals such as business analysts or requirements engineers can leverage CTA techniques to elicit details about cognitive processes and internal mental models, which can then be incorporated into to functionally accurate and subjectively pleasing designs.
For further reading, I would suggest the entire text of “Cognitive Minds”. It provides a really good explanation of CTA theories and methods. It also provides good practical examples of the application of CTA techniques.
Crandall, Beth; Klein, Gary; Hoffman, Robert R. "Understanding How Consumers Make Decisions: Using Cognitive Task Analysis for Market Research." Working Minds: A Practitioner’s Guide to Cognitive Task Analysis. Cambridge, Mass.: MIT Press, 2006. 215-228.
Summary / Assessment
In this chapter of Working Minds, the authors demonstrate how the application of cognitive task analysis (CTA) techniques to market research can help clarify various cognitive questions such as: “How do consumers decide whether to purchase a product?” “How do consumers make sense of what the product does or how it works?” -and- “When consumers have developed incorrect or inadequate mental models, what would help them shift to better models so they are more satisfied with the product?” The use of CTA techniques augments the traditional approach of doing market research by enabling us to discover the way consumers think about using and buying products, and how consumers make decisions. (The traditional approach is relegated to studying consumer beliefs, attitudes, and preferences.)
The traditional way of performing market research is to ask consumers how they make their purchasing decisions. This technique is incomplete because consumers are sometimes unable to explicitly express their cognitive processes when choosing a particular product. We must ask the appropriate questions and observe the consumer “in the wild” to infer their decision strategy. CTA techniques also provide a means to elicit and document the internal mental models consumers build about products. Their mental models include how they perceive the product (behaviorally, emotionally, and reflectively), and what makes the product effective or ineffective. In learning about product effectiveness, we also learn about how the product is being used and in what context it is being used.
The authors define the following three basic strategies for applying CTA methods to market research: Concurrent Observations and Interviews, Simulations and Props, and Retrospective Interviews. Concurrent Observations and Interviews are used while the consumers are making product decisions and while the consumers are interacting with products. They are used to record what users are thinking about in situ (not to record remembrances or hypothetical cases). Simulations and Props are used when it isn’t possible to observe consumers in action. We can use this technique to “probe consumer cognition”, to see what consumers were picking up on, and what they were considering or ignoring. Retrospective Interviews can be leveraged when trying to understand reflective issues such as brand loyalty and what prompts users to switch brands. (For example, the consumer’s history with the product is identified and examined in such interviews.)
Just as it is difficult for Consumers to express their cognitive processes when choosing or using a particular product, Users have difficulties when expressing the complex cognitive processes involved in their profession. Herein lies the nexus between studying consumer behavior and designing effective software products. Users may express what they think they need (new technologies, certain design scheme, etc.), but in reality it is hard for users to express -or translate- their true needs into exact specifications. Professionals such as business analysts or requirements engineers can leverage CTA techniques to elicit details about cognitive processes and internal mental models, which can then be incorporated into to functionally accurate and subjectively pleasing designs.
For further reading, I would suggest the entire text of “Cognitive Minds”. It provides a really good explanation of CTA theories and methods. It also provides good practical examples of the application of CTA techniques.
Wednesday, February 16, 2011
A Summary of "Chapter 3: Natural Interaction" from "The Design of Future Things"
Citation
Norman, Donald A. "Chapter 3: Natural Interaction." The Design of Future Things. New York: Basic Books, 2007. 57-90.
Summary / Assessment
In this chapter of the “Design of Future Things”, Donald Norman discusses ideas surrounding the incorporation of natural communication within designs. He draws a distinction between communication and signaling. He writes that “interactive” devices of today signal their users, rather than provide an effective means of natural communication. A dishwasher beeps when the dishes are done. A microwave beeps when food is ready. Such signals may be useful in isolation, but a cacophony of these types of signals may prove to be distracting, un-interpretable, and potentially dangerous. We should use more natural communication and sounds in our designs. Natural sounds (i.e. - sounds we encounter everyday, not sounds generated by an electronic device) can provide the location of an object, reveal their composition, and reveal their activity. The primary example of natural sound/interaction given by Norman is the whistling tea kettle.
This natural communication is referred to as implicit communication. Implicit communication also includes communication afforded by the natural side effects of people’s activities. The messy research laboratory provides the implicit signal that it is being used. Footprints in the sand implicitly tell us that someone has passed by earlier. The presence of sticky notes or underlined passages in a book tells us that the book has been read. These “non-purposeful” clues can inform us of what is happening or what has happened, provide awareness of the environment, and let us know if we should take action or continue on with what we are doing.
Affordances are “the range of activities an animal or person can perform upon an object in the world.” For example: a chair affords sitting or hiding-behind for an adult, but not for an elephant. Affordances are not attributes of an object, but rather, relationships between agents and objects. Affordances exist whether or not they have been discovered; the design challenge is to make affordances apparent to users. If affordances are easily apparent, they guide users’ behavior, and they make object interaction intuitive and natural.
Interaction with autonomous, intelligent devices is particularly challenging because communication has to go both ways (person-to-machine and machine-to-person). Norman offers horseback riding as a good example of interaction between intelligent agents. An important aspect of horseback riding is “tight-reign” and “loose-reign” control. In tight-reign control, power shifts from the horse to the rider; in loose-reign control, power shifts from the rider to the horse. Loose-reign control allows the horse to be more autonomous; however the rider can still provide some oversight through natural interaction of verbal commands, and heel kicks. This idea of allowing the natural variance of independence and interaction is powerful and can be incorporated into designing human-machine interactions.
In the remainder of the chapter, Norman makes a few other points germane to the design of human-machine interaction.
Norman, Donald A. "Chapter 3: Natural Interaction." The Design of Future Things. New York: Basic Books, 2007. 57-90.
Summary / Assessment
In this chapter of the “Design of Future Things”, Donald Norman discusses ideas surrounding the incorporation of natural communication within designs. He draws a distinction between communication and signaling. He writes that “interactive” devices of today signal their users, rather than provide an effective means of natural communication. A dishwasher beeps when the dishes are done. A microwave beeps when food is ready. Such signals may be useful in isolation, but a cacophony of these types of signals may prove to be distracting, un-interpretable, and potentially dangerous. We should use more natural communication and sounds in our designs. Natural sounds (i.e. - sounds we encounter everyday, not sounds generated by an electronic device) can provide the location of an object, reveal their composition, and reveal their activity. The primary example of natural sound/interaction given by Norman is the whistling tea kettle.
This natural communication is referred to as implicit communication. Implicit communication also includes communication afforded by the natural side effects of people’s activities. The messy research laboratory provides the implicit signal that it is being used. Footprints in the sand implicitly tell us that someone has passed by earlier. The presence of sticky notes or underlined passages in a book tells us that the book has been read. These “non-purposeful” clues can inform us of what is happening or what has happened, provide awareness of the environment, and let us know if we should take action or continue on with what we are doing.
Affordances are “the range of activities an animal or person can perform upon an object in the world.” For example: a chair affords sitting or hiding-behind for an adult, but not for an elephant. Affordances are not attributes of an object, but rather, relationships between agents and objects. Affordances exist whether or not they have been discovered; the design challenge is to make affordances apparent to users. If affordances are easily apparent, they guide users’ behavior, and they make object interaction intuitive and natural.
Interaction with autonomous, intelligent devices is particularly challenging because communication has to go both ways (person-to-machine and machine-to-person). Norman offers horseback riding as a good example of interaction between intelligent agents. An important aspect of horseback riding is “tight-reign” and “loose-reign” control. In tight-reign control, power shifts from the horse to the rider; in loose-reign control, power shifts from the rider to the horse. Loose-reign control allows the horse to be more autonomous; however the rider can still provide some oversight through natural interaction of verbal commands, and heel kicks. This idea of allowing the natural variance of independence and interaction is powerful and can be incorporated into designing human-machine interactions.
In the remainder of the chapter, Norman makes a few other points germane to the design of human-machine interaction.
- Be Predictable. - Intelligent machines of the future should not attempt to read user’s minds or predict users’ next actions. There are two issues in doing this: firstly, predictions could be wrong, and secondly, it makes the machine’s actions unpredictable. Unpredictability leads to the user guessing at what the machine is trying to do.
- Don’t distance users from implicit communication. – Today’s automobile isolates its users from certain implicit communication, thereby reducing situational awareness. The user relies more and more on the technology in the automobile (such as automatic lane-keeping). This distancing and reliance can potentially make the automobile more dangerous to operate.
- The best designs compensate human intelligence, rather than supersede it. – For example: power-assisted devices can augment human capabilities, but they also can limit human capabilities where needed.
Thursday, February 10, 2011
A Framework for Online Charitable Giving
This paper presents a framework that can be utilized by charitable organizations to increase online giving. The framework offers a systematic approach to thinking about online giving, and provides tools to construct robust mechanisms for eliciting and collecting donations. The framework consists of three components: a persuasive design component, an emotional design component, and a donation-usability component.
Direct Link to PDF
http://romalley2000.home.comcast.net/documents/OMalley_Charitable_Giving_Framework.pdf
Embedded PDF
Direct Link to PDF
http://romalley2000.home.comcast.net/documents/OMalley_Charitable_Giving_Framework.pdf
Embedded PDF
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