Machine Understanding and Avoidance of Misunderstanding in Agent-Directed Simulation and in Emotional Intelligence

Total Page:16

File Type:pdf, Size:1020Kb

Machine Understanding and Avoidance of Misunderstanding in Agent-Directed Simulation and in Emotional Intelligence Machine Understanding and Avoidance of Misunderstanding in Agent-directed Simulation and in Emotional Intelligence Tuncer Ören1, Mohammad Kazemifard2 and Levent Yilmaz3 1School of Electrical Eng. & Computer Science,University of Ottawa, 800 King Edward Ave.,Ottawa,ON,Canada 2Department of Computer Engineering, Razi University, Kermanshah, Iran 3Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, U.S.A. Keywords: Agents with Emotional Intelligence, Emotional Intelligence Simulation, Machine Understanding, Avoidance of Misunderstanding. Abstract: Simulation is being applied in many very important projects and often it is a vitally important infrastructure for them. Several types of computational intelligence techniques have been part of the abilities of simula- tion. An important aspect of intelligence is the ability to understand. Agent-directed simulation (ADS) is a comprehensive paradigm to cover all aspects of synergy of software agents and simulation and our approach is to develop agents with understanding abilities. After a brief review of ADS, our paradigms of machine understanding is presented. The article clearly indicates types of misunderstandings that might occur. Our research plans are to avoid some of the misunderstandings which could occur and especially to have self- attesting abilities in our applications to document which types of misunderstandings are avoided. 1 INTRODUCTION After this introduction, we start with a concise review of our view of agent directed simulation Simulation is being applied in many very important which provides a comprehensive framework to projects and often it is a vitally important infrastruc- consider all aspects of the synergy of software ture for them. Several types of computational intelli- agents and simulation. In section 3, we discuss our gence techniques have been part of the abilities of four paradigms for machine understanding. Section simulation (Ören, 1995); (Yilmaz and Ören, 2009). 4 covers a systemati-zation of most types of An important aspect of intelligence is the ability misunderstanding appli-cable to machine to understand. Our research on machine understand- understanding. Sections 5 covers the conclusions ing started with understanding of simulation pro- and some of our plans for future research. grams (Ören et al., 1990) and evolved to understand- ing software in general (Ören, 1992), then to understanding systems (Ören, 2000), to agents with 2 AGENT-DIRECTED ability to understand emotions (Kazemifard et al., SIMULATION 2009), and finally to machine understanding in emo- tional intelligence simulation (Kazemifard, et al., Agent-Directed Simulation (ADS) is a unifying and 2013). comprehensive framework that allows integration of Failure avoidance has been recently intro-duced agent and simulation technologies (Ören, 2000). to advanced simulation studies as a paradigm in Agents are often considered as model design addition to validation and veri-fication studies (Ören metaphors in the development of simulations. Yet, and Yilmaz 2009). this narrow view limits the potential of agents in This article is built on our previous work improving various other dimensions of simulation especially on machine understanding as applied to (Yilmaz and Ören, 2009). To this end, ADS is agents used in simulation and to misunderstanding. comprised of three distinct, yet related areas that can However, in this article, we provide three additional be grouped under two categories as follows: machine understanding paradigms in addition to our . Simulation for Agents (i.e., agent simulation) basic machine understanding paradigm. Ören T., Kazemifard M. and Yilmaz L.. 318 Machine Understanding and Avoidance of Misunderstanding in Agent-directed Simulation and in Emotional Intelligence. DOI: 10.5220/0004635003180327 In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013), pages 318-327 ISBN: 978-989-8565-69-3 Copyright c 2013 SCITEPRESS (Science and Technology Publications, Lda.) MachineUnderstandingandAvoidanceofMisunderstandinginAgent-directedSimulationandinEmotionalIntelligence involves the use of simulation modeling method- 3 MACHINE methodology and technologies to analyze, design, model, simulate, and test agent systems. This UNDERSTANDING includes, but is not limited to using agents as model design elements (i.e., agent-based In the study of natural phenomena, the role of simu- modeling). lation is often cited as “to gain insight” which is . Agents for Simulation: (1) Agent-supported another way of expressing “to under-stand.” Under- standing is one of the important philosophical topics. simulation involves the use of agents as support facilities to enable computer assistance in From a pragmatic point of view, it has a broad appli- simulation-based problem solving (e.g., simulation cation potential in many computerized studies in- cluding program understanding, machine vision, experiment management); (2) Agent-based simulation, on the other hand, focuses on the use fault detection based on machine vision as well as of agents for the generation of model behavior situation awareness and assessment. Therefore, sys- tematic studies of the elements, structures, archi- (e.g., simulator coordination, run-time models) in a simulation study as well as agent-initiated tectures, and scope of applications of com-puterized simulation. understanding systems as well as the characteristics of the results (or products) of understanding pro- In agent simulation, agents possess high-level cesses are warranted. mechanisms that include communication protocols Dictionary definitions of “to understand” include for interaction, task allocation, coordination of the following: to seize the meaning of, to accept as a actions, and conflict resolution at varying levels of fact, to believe, to be thoroughly acquainted with, to sophistication. Agent-based simulation focuses on form a reasoned judgment concerning something, to the use of agent technology to monitor and generate have the power of seizing meanings, forming rea- model behavior. This is similar to the use of soned judgments, to appreciate and sympathize with, Artificial Intelligence techniques for the generation to tolerate, and to possess a passive knowledge of a of model behavior (e.g., qualitative simulation and language. knowledge-based simulation). Agents can provide For machine understanding, or computerized un- cognitive architectures that allow reasoning and derstanding, we aim a limited scope as was ex- planning and serve as run-time models of simulation pressed in a previous publication: “We say that a model behavior management such as dynamic model system ‘knows about’ a class of objects, or relations, updating and symbiotic simulation. That is, context- if it has an internal relation for the class which ena- awareness of intelligent agents can facilitate bles it to operate on objects in this class and to simulator coordination, where runtime decisions for communicate with others about such operations. model staging and updating takes place to facilitate Thus, if a system knows about X, a class of objects dynamic composability. On the other hand, agent- or relations on objects, it is able to use an (internal) supported simulation enables the use of agents to representation of the class in at least the following support simulations as well as simulation studies by ways: receive information about the class, generate enhancing cognitive capabilities in problem elements in the class, recognize members of the specification, simulation experiment management, class and discriminate them from other class mem- and behavior analysis. bers, answer questions about the class, and take into Often, agent-supported simulation is used for the account information about changes in the class following purposes (Yilmaz and Ören, 2009): members” (Zeigler 1986)." (Ören, 2000). For addi- . To provide computer assistance for frontend and/or tional clarification of understanding and its philo- backend interface functions; sophical roots see Ören (2000). to process elements of a simulation study symbolically (for example, for consistency checks 3.1 Machine Understanding: and built-in reliability); and Basic Paradigm . to provide cognitive abilities to the elements of a simulation study, such as learning or As seen in Figure 1, an understanding system re- understanding abilities. quires the provision of a meta-model, the perception of the source in terms of the elements and constrains depicted in the meta-model, and an analyser that allows mapping of the perceived elements to con- structs of the meta-model. In this context, a model is an abstraction of phenomena or system, whereas a 319 SIMULTECH2013-3rdInternationalConferenceonSimulationandModelingMethodologies,Technologiesand Applications meta-model provides an abstraction of the properties of the model itself. A model conforms to its meta- B: entity to be understood model in the way that a computer program conforms to the grammar of the programming language in Understanding system A which it is developed. Analyzer D: perception of B (by A) Entity B with respect to C can understand C: meta-model of Bs Evaluator (interprets the relationship Cs: System A Ds: percep- (meta-knowledge meta-models tions about Bs) between C & D) 1 2 3 U: an understanding of B by A Relationships: C(s)-D(s) with respect to C Figure 1: Machine understanding – Basic paradigm. Figure 2: Functional decomposition of an under-standing
Recommended publications
  • Perceptions of Teacher Expectations Among First and Second
    Digital Commons @ George Fox University Doctor of Education (EdD) Theses and Dissertations 12-1-2016 Student Voice: Perceptions of Teacher Expectations Among First and Second Generation Vietnamese and Mexican Students Sara Gandarilla George Fox University, [email protected] This research is a product of the Doctor of Education (EdD) program at George Fox University. Find out more about the program. Recommended Citation Gandarilla, Sara, "Student Voice: Perceptions of Teacher Expectations Among First and Second Generation Vietnamese and Mexican Students" (2016). Doctor of Education (EdD). 90. http://digitalcommons.georgefox.edu/edd/90 This Dissertation is brought to you for free and open access by the Theses and Dissertations at Digital Commons @ George Fox University. It has been accepted for inclusion in Doctor of Education (EdD) by an authorized administrator of Digital Commons @ George Fox University. For more information, please contact [email protected]. STUDENT VOICE: PERCEPTIONS OF TEACHER EXPECTATIONS AMONG FIRST AND SECOND GENERATION VIETNAMESE AND MEXICAN STUDENTS By SARA GANDARILLA FACULTY RESEARCH COMMITTEE: Chair: Terry Huffman, Ph.D. Members: Ginny Birky, Ph.D. and Tatiana Cevallos, Ed.D. Presented to the College of Education, George Fox University In partial fulfillment of the requirements for the degree of Doctor of Education December 7, 2016 ii ABSTRACT This qualitative research study explored the perceptions first and second generation Vietnamese and Mexican high school students hold on teacher expectations based on their racial identity. Specifically, this study explores the critical concepts of stereotype threat, halo effect, and self-fulfilling prophecy. The primary purpose of this investigation was to enhance the understanding of how the perception students have impacts success or lack of success for two different student groups.
    [Show full text]
  • Positive Stereotypes, Negative Outcomes: Reminders of the Positive Components of Complementary Gender Stereotypes Impair Perform
    1 British Journal of Social Psychology (2018) © 2018 The British Psychological Society www.wileyonlinelibrary.com Positive stereotypes, negative outcomes: Reminders of the positive components of complementary gender stereotypes impair performance in counter-stereotypical tasks Rotem Kahalon1* , Nurit Shnabel1 and Julia C. Becker2 1Tel-Aviv University, Israel 2Osnabruck University, Germany Gender stereotypes are complementary: Women are perceived to be communal but not agentic, whereas men are perceived to be agentic but not communal. The present research tested whether exposure to reminders of the positive components of these gender stereotypes can lead to stereotype threat and subsequent performance deficits on the complementary dimension. Study 1 (N = 116 female participants) revealed that compared to a control/no-stereotype condition, exposure to reminders of the stereotype about women’s communality (but not to reminders of the stereotype about women’s beauty) impaired women’s math performance. In Study 2 (N = 86 male participants), reminders of the stereotype about men’s agency (vs. a control/no- stereotype condition) impaired men’s performance in a test of socio-emotional abilities. Consistent with previous research on stereotype threat, in both studies the effect was evident among participants with high domain identification. These findings extend our understanding of the potentially adverse implications of seemingly positive gender stereotypes. While the message that negative stereotypes are anti-egalitarian and socially unacceptable is reinforced in contemporary Western society, positive stereotypes are prevalent and considered socially acceptable (Czopp, Kay, & Cheryan, 2015). The present research tested whether, despite their subjectively positive tone, positive gender stereotypes might lead to negative outcomes. In particular, we examined whether highlighting the positive stereotypes about women’s communality and men’s agency can lead to stereotype threat effects.
    [Show full text]
  • Does Physical Attractiveness and Sex Impact Decisions in a Threat Detection Task
    DOES PHYSICAL ATTRACTIVENESS AND SEX IMPACT DECISIONS IN A THREAT DETECTION TASK A research project submitted in partial fulfilment of the requirements for the Degree of MASTER OF SCIENCE IN APPLIED PSYCHOLOGY At the UNIVERSITY OF CANTERBURY By JESSIE KENDALL UNIVERSITY OF CANTERBURY 2014 i ACKNOWLEDGEMENTS I would firstly like to thank Deak Helton for being an excellent and supportive supervisor. I am very grateful of your guidance and your efficient and thorough feedback that you have provided throughout the year. Also, to Kumar Yogeeswaran, my secondary supervisor, for your exceptional guidance and assistance. It has been a privilege to work with you both. A very big thank you also to Jon Wiltshire, who helped design and create the computer simulation game necessary for this research. I would also like to thank Christopher Burt for encouraging me to return to University to complete the second year of the APSY course after a break. 1 CONTENTS CONTENTS .............................................................................................................................................. 1 LIST OF TABLES .................................................................................................................................... 3 ABSTRACT .............................................................................................................................................. 4 SECTION I – INTRODUCTION .......................................................................................................... 5 1.1. STEREOTYPES
    [Show full text]
  • Cognitive Biases in Software Engineering: a Systematic Mapping Study
    Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases.
    [Show full text]
  • Infographic I.10
    The Digital Health Revolution: Leaving No One Behind The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond. But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations. 59 Jan 2021 % of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % % ...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting depression % back to normal (updated April and/or anxiety.5 2021).4 Up to of consumers now interested in telehealth going forward. $250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6 The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and mental health conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 lifestyle changes and regular health screenings9 Feb. 2020 Apr. 2020 OCCAM’S RAZOR • CONJUNCTION FALLACY • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BELIEF BIAS • INFORMATION BIAS Digital health ecosystems are transforming• AMBIGUITY BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE PSYCHOLOGY • SYSTEM JUSTIFICATION • BACKFIRE EFFECT • ENDOWMENT EFFECT • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory.
    [Show full text]
  • John Collins, President, Forensic Foundations Group
    On Bias in Forensic Science National Commission on Forensic Science – May 12, 2014 56-year-old Vatsala Thakkar was a doctor in India but took a job as a convenience store cashier to help pay family expenses. She was stabbed to death outside her store trying to thwart a theft in November 2008. Bloody Footwear Impression Bloody Tire Impression What was the threat? 1. We failed to ask ourselves if this was a footwear impression. 2. The appearance of the impression combined with the investigator’s interpretation created prejudice. The accuracy of our analysis became threatened by our prejudice. Types of Cognitive Bias Available at: http://en.wikipedia.org/wiki/List_of_cognitive_biases | Accessed on April 14, 2014 Anchoring or focalism Hindsight bias Pseudocertainty effect Illusory superiority Levels-of-processing effect Attentional bias Hostile media effect Reactance Ingroup bias List-length effect Availability heuristic Hot-hand fallacy Reactive devaluation Just-world phenomenon Misinformation effect Availability cascade Hyperbolic discounting Recency illusion Moral luck Modality effect Backfire effect Identifiable victim effect Restraint bias Naive cynicism Mood-congruent memory bias Bandwagon effect Illusion of control Rhyme as reason effect Naïve realism Next-in-line effect Base rate fallacy or base rate neglect Illusion of validity Risk compensation / Peltzman effect Outgroup homogeneity bias Part-list cueing effect Belief bias Illusory correlation Selective perception Projection bias Peak-end rule Bias blind spot Impact bias Semmelweis
    [Show full text]
  • The Halo Effect in Sports
    International Journal of Business and Social Science Volume 9 • Number 2 • February 2018 The Halo Effect in Sports Prof. Dr. Gerd NUFER Natalie ALESI Reutlingen University ESB Business School Germany Abstract A halo effect can lead to significantly biased and distorted judgments in numerous situations and settings in daily life. However, its impact has barely been researched in the sporting environment, although it might help a great deal in understanding how sport fans think and behave. This paper provides an international literature review on the halo effect in different research fields. Built upon this state of the art, an empirical study based on two Ger- man soccer clubs, VfB Stuttgart and FC Bayern Munich, analyzes the presence of halo effects explained by social identity theory. The study shows that supporters rate aspects of the respective team, for example the president’s competency, more favorable than common sport spectators, and this effect even increases with a higher level of team identification. Keywords: halo effect, heuristic, cognitive bias, sports, sport management, sport marketing, soccer, football 1Introduction People make numerous judgments and decisions every day. These might be only small ones like deciding what to wear or which dish to choose, but also more relevant ones like what to study or which business strategy to follow. People usually take it for granted that their judgments and decisions are based on objective and logical thinking. The truth is, however, that human thinking is quite often unconsciously influenced by cognitive biases. Cognitive biases can be seen as errors in thinking that lead to distorted decisions and judgments (Kahneman, 2012).
    [Show full text]
  • Why Do Humans Reason? Arguments for an Argumentative Theory
    University of Pennsylvania ScholarlyCommons Goldstone Research Unit Philosophy, Politics and Economics 4-2011 Why Do Humans Reason? Arguments for an Argumentative Theory Hugo Mercier University of Pennsylvania, [email protected] Dan Sperber Follow this and additional works at: https://repository.upenn.edu/goldstone Part of the Epistemology Commons, and the Psychology Commons Recommended Citation Mercier, H., & Sperber, D. (2011). Why Do Humans Reason? Arguments for an Argumentative Theory. Behavioral and Brain Sciences, 34 (2), 57-74. http://dx.doi.org/10.1017/S0140525X10000968 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/goldstone/15 For more information, please contact [email protected]. Why Do Humans Reason? Arguments for an Argumentative Theory Abstract Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views.
    [Show full text]
  • Omission Neglect and the Bias Blind Spot: Effects of the Self-Other Asymmetry in Susceptibility to Bias and Responsiveness to Debiasing
    Omission Neglect and the Bias Blind Spot: Effects of the Self-Other Asymmetry in Susceptibility to Bias and Responsiveness to Debiasing A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati In partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY (PH.D.) IN THE DEPARTMENT OF MARKETING of the College of Business 2011 By Xiaoqi Han M.A., Marquette University Committee Chair: Frank R. Kardes, Ph.D. i ABSTRACT Omission Neglect and the Bias Blind Spot: Effects of the Self-Other Asymmetry in Susceptibility to Bias and Responsiveness to Debiasing By Xiaoqi Han Chair: Dr. Frank R. Kardes Research on the bias blind spot shows that people are less capable of identifying biases in their own judgment than in others. People generally believe that they are less susceptible to biases than their peers and average people. This dissertation investigates the self-other bias asymmetry with respect to omission neglect. Omission neglect refers to insensitivity to missing or unknown information. Results from bias blind spot research imply a potential relationship between the self-other asymmetry in bias belief and omission neglect. Research on bias correction suggests that people holding a higher degree of asymmetry in bias beliefs may be less likely to correct biases even when omissions are made noticeable. Ironically, consumers who believe that they are less vulnerable to omission neglect may be more susceptible to omission neglect. Consumers may be also less likely to correct their judgment even when omitted information is made noticeable. The goal of the dissertation is to develop debiasing techniques to debias omission neglect in order to improve consumer judgment and decision making.
    [Show full text]
  • Heuristics and Cognitive Biases in Decision Making Process
    Heuristics and Cognitive Biases in Decision Making Process Anna Marta Winniczuk Kozminski University [email protected] EXTENDED ABSTRACT The goal of this paper is to recognize the most commonly present heuristics and cognitive biases occurring during managerial decision making process. The study will use simulation game as a space to track how game participants are perceiving their biases and heuristics during the game. The purpose of the study is to identify the most commonly used cognitive biases and heuristics in decision making process and compare them to performance of the team and their decisions in the simulation game, which will give valuable insights that can improve game-based business learning. Decision making is one of the crucial processes occurring every day in every organization. The process of making decisions in business situations is influenced by a variety of different circumstances, thus the subject is widely discussed in both academic and professional publications. The subject of heuristics and cognitive biases was widely introduced into decision making science by David Kahneman and Tversky (Tversky & Kahneman, 1973) and furtherly researched by other authors(Baron & Hershey, 1988); Griffin, D., Gonzalez, R., & Varey, C., 2001). In 2011, David Kahneman, Dan Lovallo and Oliver Sibony in their article “The Big Idea: Before You Make That Big Decision…” published in Harvard Business Review listed a checklist for managers to avoid cognitive biases in their decision making process. The most common cognitive biases in managerial decisions are: confirmation bias, availability bias, anchoring, halo effect , overconfidence, disaster neglect and loss aversion (Kahneman & Sibony, 2011). Mentioned above biases are widely common in managerial decision making process and worth examining in an experimental learning environment of a simulation game.
    [Show full text]
  • Debiasing Halo Effect: Auditor Reputation and the Role of Auditing Learning on Financial Statements User Trust
    International Research Journal of Management, IT & Social Sciences Available online at https://sloap.org/journals/index.php/irjmis/ Vol. 7 No. 6, November 2020, pages: 33-41 ISSN: 2395-7492 https://doi.org/10.21744/irjmis.v7n6.1006 Debiasing Halo Effect: Auditor Reputation and the Role of Auditing Learning on Financial Statements User Trust Radian Sri Rama a Sutrisno T b Erwin Saraswati c Aulia Fuad Rahman d Article history: Abstract The purpose of this study was to examine the effect of auditor reputation (big Submitted: 09 July 2020 four and non-big four) and auditing learning (with audit learning and without Revised: 18 August 2020 audit learning) on the trust of users of financial statements. Testing the Accepted: 27 September 2020 influence of the auditor's reputation and learning will show a halo effect on users of financial statements. The research method used was an experiment. Participants were 102 students. Data analysis was performed with the two-way Anova test. The results showed a halo effect so that the auditor's reputation and Keywords: audit learning affected the users of financial statements. auditing learning; auditor reputation; financial statements; halo effect; International research journal of management, IT and social sciences © 2020. trust; This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Corresponding author: Radian Sri Rama, Accounting Department, Brawijaya University, Indonesia. Email address: [email protected] a Accounting Department, Brawijaya University, Indonesia b Accounting Department, Brawijaya University, Indonesia c Accounting Department, Brawijaya University, Indonesia d Accounting Department, Brawijaya University, Indonesia 33 34 ISSN: 2395-7492 1 Introduction User trust in financial statements is obtained based on confidence in quality audits (Rodgers et al., 2019).
    [Show full text]
  • Too Much Information Not Enough Meaning Need to Act Fast What
    T H E C O G N I T I V E B I A S C O D E X – Kognitive Verzerrungen We store memories differently based We notice things already primed in on how they were experienced Too Much What Should We memory or repeated often We reduce events and lists Tip of the tongue phenomenon Bizarre, funny, visually striking, or Information Remember? to their key elements Levels–of–processing effect anthropomorphic things stick out more than non-bizarre/unfunny things Absent–mindedness Next–in–line effect Part–set cueingSerial–position effect effect Google effect Testing effect Memory inhibition We discard specifics Recency effect Suffix effect Leveling and sharpening Primacy effect DurationModality neglect effect MisinformationList–length effect effect to form generalities Serial recall effect We notice when something has changed Availability heuristic Attentional bias Illusory truth effect Mere–exposure effect Context effect Cue–dependent forgetting Mood–congruent memory bias Frequency illusion Baader–Meinhof Phenomenon Peak–end rule Empathy gap Fading affect bias Omission bias Base rate fallacy Bizarreness effect Negativity bias Humor effect Von Restorff effect We edit and reinforce ImplicitStereotypical stereotypes bias Picture superiority effect Self–relevance effect Negativity bias some memories after the fact Implicit associationPrejudice Anchoring Conservatism Contrast effect Spacing effect Distinction bias Focusing effect Suggestibility Framing effect We are drawn to details False memory Money illusion Misattribution of memory Weber–Fechner law SourceCryptomnesia
    [Show full text]