Searching for Herbert Simon

2013 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Michael F. Gorman ◽  
Donald E. Wynn ◽  
William David Salisbury

Since Herbert Simon’s seminal work (Simon, 1957) on bounded rationality researchers and practitioners have sought the “holy grail” of computer-supported decision-making. A recent wave of interest in “business analytics” (BA) has elevated interest in data-driven analytical decision making to the forefront. While reporting and prediction via business intelligence (BI) systems has been an important component to business decision making for some time, BA broadens its scope and potential impact in business decision making further by moving the focus to prescription. The authors see BA as the end-to-end process integrating the production through consumption of the data, and making more extensive use of the data through heavily automated, integrated and advanced predictive and prescriptive tools in ways that better support, or replace, the human decision maker. With the advent of “big data”, BA already extends beyond internal databases to external and unstructured data that is publicly produced and consumed data with new analytical techniques to better enable business decision makers in a connected world. BI research in the future will be broader in scope, and the challenge is to make effective use of a wide range of data with varying degrees of structure, and from sources both internal and external to the organization. In this paper, we suggest ways that this broader focus of BA will also affect future BI research streams.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.


2021 ◽  
Author(s):  
◽  
Edward Johnson

<p><b>The gold mining industry in Ghana is characterised by complexity in terms of its extended/sequential operations, its system-wide reach, its multiple stakeholders, and the variety of formal and informal organisations that constitute the industry. Perceptions of the industry differ considerably amongst stakeholders, depending on their stakes and interests, knowledge, understanding, involvement and agency within or without the sector. Studies of the industry to date have overlooked these diverse viewpoints and used limited-scope, single-frame analyses. However, they have highlighted wide-ranging industry issues that impact the diversity of stakeholders, which could benefit from a fuller and more comprehensive analysis.</b></p> <p>This study addresses this need by adopting a multi-framing systems-based approach. Data was examined and analysed through a variety of systems-based lenses and frames, including a stakeholder analysis (SA) frame, a causal loop modelling (CLM) frame, supply chain analysis (SCA) frame and the Theory of Constraints (TOC) Thinking Processes analytical frames lenses. First the Current Reality Tree (CRT) tool of the Theory of Constraints (TOC) was used to synthesise information from the literature examined, providing an initial provisional CRT model. Interview data was collected by sharing and seeking feedback to the CRT model at multiple levels of the industry, giving voice to stakeholders throughout the sector. Subsequent analysis used all the modelling frameworks mentioned above in a multi-framing analysis.</p> <p>In particular, the evaporating cloud (EC) tool from TOC was used to structure and develop potential solutions to conflict highlighted by the literature review, the SA, SCA and CLM. Building on this, a final CRT was developed, and a goal tree (GT) used to design the desired future whilst employing the future reality tree (FRT) to test the plausibility of solutions from the EC to deliver the desired future. The prerequisite tree (PRT) was then used to identify obstacles and intermediate objectives that must be overcome for successful transition to the desired future.</p> <p>Insights from the research shows a desire by multi-national large scale-gold mining companies and government alike to minimise adverse impacts and maximise the sector’s outcomes for key stakeholders, including those at the community level. However, the research has documented many instances of actions taken to address issues and improve outcomes that have instead resulted in unresolved dilemmas and paradoxes, failing to achieve desired outcomes.</p> <p>A number of factors have been identified as being responsible for these situations. Key amongst them is a limited understanding to deliver desired outcome for stakeholders without compromises, a focus on short-term goals, no collective effort, and arms-length/win-lose relationships amongst the Ghanaian stakeholders of the industry.</p> <p>The study’s concluding findings and results allow decision makers to benefit significantly from the study through its recommendations and showcasing of tools that may allow them to make sound decisions and address endogenous and exogenous cause-effect relationships limiting desirable outcomes from actions taken.</p> <p>Theoretical and knowledge-based contributions are made by conceptualising and offering evidence for three key factors or dimensions that can explain a significant number of issues limiting desirable outcomes for stakeholders of the gold mining industry. These include difficulty to transition from theory (espoused aims) to practice, a relative focus on local optima (silo thinking), poor monitoring (lack of evaluation), and a control culture. Methodological contributions are made by demonstrating the application of a multi-framing approach in a more organic and iterative manner as opposed to its use in a designed sequence, working down through layers of various systemic levels of an industry (in this case, the gold mining industry in Ghana). By so doing, the study builds on and extends the practicality of the multi-framing approach and stimulates further research in the field.</p> <p>In terms of its contribution to practice, the study provides Government, political and mining sector policy decision makers, and other interested actors, with a platform for understanding the sector in order to support their decision making about the industry to ultimately improve outcomes for key stakeholders. In particular, the study allows mining sector policy decision makers and other stakeholders to recognise complexity, uncertainty and conflicts that are embedded in the mining system and in their everyday decision-making activities about the industry. It also allows these stakeholders to become more aware that such issues can be addressed and improved by identifying and focusing on one or few underlying causes.</p> <p>This thesis draws on systems-based frameworks drawn both from functional management, for example, the supply chain and value chain frameworks of operations management and the stakeholder framework of strategic management, and from the broad domain of systems thinking (ST) and systems-based methodologies; and then focuses on the intersection of these frameworks in relation to the gold mining sector in Ghana. Due to the wide range of techniques applied, none are over-explored, creating potential for further research. On the other hand, with regard to explanations, depending on background, practitioners, and researchers familiar with some techniques may consider those sections over-explained. The researcher has sought a balance for the purpose of this study. Whilst limiting the scope of this work has been necessary in the context of doctoral study, topics ripe for future research are set out in the conclusion.</p>


Author(s):  
Tanushri Banerjee ◽  
Arindam Banerjee

There are several challenges faced by decision makers while deploying Business Analytics in their organization. There may not be one resolution approach that is suitable for creating a Business Analytics culture in all organizations. However, it is easy to perceive that most India-based organizations may have similar issues of data organization that may be impeding their progression in the field of Analytics. Based on their research, the authors have proposed a framework for adoption of Analytics in Indian firms in their book “Weaving Analytics for Effective Decision Making” by SAGE. They propose to use that model for explaining certain domain specific adoption of Business Analytics in organizations in India. They have used a case study of a Global Bank which is in the process of establishing its consumer lending USA operations, an offshore captive operation, in India to describe the process of building an Analytics team in an organization in India. Data processed using R has been added as screenshots for supporting the findings.


Author(s):  
Pedro Caldeira Neves ◽  
Jorge Rodrigues Bernardino

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-26
Author(s):  
Friederike Wall

Coordination among decision-makers of an organization, each responsible for a certain partition of an overall decision-problem, is of crucial relevance with respect to the overall performance obtained. Among the challenges of coordination in distributed decision-making systems (DDMS) is to understand how environmental conditions like, for example, the complexity of the decision-problem to be solved, the problem’s predictability and its dynamics shape the adaptation of coordination mechanisms. These challenges apply to DDMS resided by human decision-makers like firms as well as to systems of artificial agents as studied in the domain of multiagent systems (MAS). It is well known that coordination for increasing decision-problems and, accordingly, growing organizations is in a particular tension between shaping the search for new solutions and setting appropriate constraints to deal with increasing size and intraorganizational complexity. Against this background, the paper studies the adaptation of coordination in the course of growing decision-making organizations. For this, an agent-based simulation model based on the framework of NK fitness landscapes is employed. The study controls for different levels of complexity of the overall decision-problem, different strategies of search for new solutions, and different levels of cost of effort to implement new solutions. The results suggest that, with respect to the emerging coordination mode, complexity subtly interferes with the search strategy employed and cost of effort. In particular, results support the conjecture that increasing complexity leads to more hierarchical coordination. However, the search strategy shapes the predominance of hierarchy in favor of granting more autonomy to decentralized decision-makers. Moreover, the study reveals that the cost of effort for implementing new solutions in conjunction with the search strategy may remarkably affect the emerging form of coordination. This could explain differences in prevailing coordination modes across different branches or technologies or could explain the emergence of contextually inferior modes of coordination.


2009 ◽  
Vol 12 (2) ◽  
pp. 111-124
Author(s):  
Chulwon Lee

The future direction of China's approach to energy policy making is, of course, difficult to predict. This is due not only to the opaque and fragmented nature of Chinese energy policy decision-making, but also to the fact that energy policy is a new topic for China's leaders and the individuals they rely on for advice to master that impinges on the interests of actors throughout the Chinese bureaucracy. The wide range of participants in the energy policy debate indicates that more diversified views on it probably reach the top leadership. The impact of the multiplicity of opinions is two-fold. It can result in more informed decision-making, but it can also delay the process as decision makers must assess a larger number of competing and sometimes contradictory views.


Author(s):  
Pascal D. König ◽  
Georg Wenzelburger

AbstractThe promise of algorithmic decision-making (ADM) lies in its capacity to support or replace human decision-making based on a superior ability to solve specific cognitive tasks. Applications have found their way into various domains of decision-making—and even find appeal in the realm of politics. Against the backdrop of widespread dissatisfaction with politicians in established democracies, there are even calls for replacing politicians with machines. Our discipline has hitherto remained surprisingly silent on these issues. The present article argues that it is important to have a clear grasp of when and how ADM is compatible with political decision-making. While algorithms may help decision-makers in the evidence-based selection of policy instruments to achieve pre-defined goals, bringing ADM to the heart of politics, where the guiding goals are set, is dangerous. Democratic politics, we argue, involves a kind of learning that is incompatible with the learning and optimization performed by algorithmic systems.


2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Dušica Sanader ◽  
Marko Laketa ◽  
Luka Laketa

In this paper we have presented analytics as a crucial factor in marketing decision making. The banking environment is turbulent and complex today. The client is well educated and his needs are constantly changing. He has access to lot of information and has power of choice. His digital expectations are high: he needs to access banking products and services from any place and at any time. Also, the client is leaving data everywhere: in bank`s database and at different internet sites. As data are comparative advantages today, the bank is eager to collect them in order to analyze data and make marketing decisions. Analytics is helping bank in this new era of doing business. Analytics assumes analysis, interpretation and communication of understandable patterns in the data. It relies on mathematics and statistics techniques in order to find new knowledge and meaning of existing data. There are many analytics techniques which are based on algorithms and databases. Depending on which problem a bank needs to solve or what it aim wants to achieve, the bank uses one or more analytical techniques. Survival Analysis, Nearest Neighbor Classification, Neural Network, Logistic Regression and Decision Tree are the most common techniques used in banking sector.Marketing analytics models support marketing decisions. Marketing models enable bank to predict outcome (e.g. if a client is likely to leave) or to identify differences between group of clients. In order to achieve results, the bank has created different marketing models such as Response Models, Queues, Retention Models, Market Basket Analysis, and Win-Back Models. Marketing models are helping the bank to predict if the client will answer on offer which bank is offering through marketing campaigns. The aim of these models is to create target group of clients or segment with likelihood to increase their relationship with the bank. In order to create marketing model, the bank defines the aim which it wants to achieve. Usually, the bank wants to keep most profitable clients and to decrease costs. After defining the aim of marketing model, the bank collects, analyses and transforms data needed for creation of the model. Also, it is necessary to estimate data quality. If data are no longer of high quality, there can be issue with model results. Also, the bank has to take into consideration the volume, velocity and variety of data. Large data are collected from lots of data sources and stored in data warehouse or data marts using modern technologies. Model technologies help to convert data into valuable information which can be used for making decisions. After creation of a model, it is necessary to estimate its accuracy, comprehensibility and level of confidence in results given by the model. Also, every model has to be managed (quarterly or yearly) in order to test if the results are still valid or it has to be changed with a new model.Analytics gives competitive advantages to the bank. It can improve effectiveness of processes and organization and improve efficiency in making marketing decisions. The bank as a profit oriented organization tends to contact profitable customer in order to increase their value through customer lifetime value. In this way, the bank has a possibility to invest in relationship with clients which can be valuable in the long run. Analytics gives knowledge about the customer. It helps to discover pattern in large amount of data. The contribution of analytics can be seen in decreasing marketing costs by identifying clients who are likely to respond on marketing campaigns. Also, it contributes in pricing, channel management, selling, segmentation and product development. Today, text analytics is also important for banking business, as lots of data are unstructured and can be found in form of documents, blogs, video sharing and comments on internet sites. In order to use this kind of data, text analytics helps the bank to understand data and read them with certain limitation. However, there are also challenges which the bank faces when implementing analytics. Limited budget, employees without necessary skills for the development of models, poor quality of data, inadequate and unintegrated softer tools, problems with protection of client data as well as imprecisely defined aim of model can be resulted in unsatisfactory realization and poor position of analytics in the bank. In order to overcome these challenges, the bank needs to set up a strategy of analytics and to link it with all the internal processes in organization.


2021 ◽  
Vol 937 (2) ◽  
pp. 022036
Author(s):  
T Podolskaya ◽  
G V Kravchenko ◽  
Kh Shatila

Abstract Environmental management accounting is a mechanism for determining and evaluating, and incorporating these cost and benefit in the day-to-day business decision making, the full spectrum of environmental costs of current production processes and the economic benefits of contamination prevention, or cleaner processes. In practice, the past 10 years have acquired significance from corporate accounting, which is the most prominent part of cost accounting. Limits were widely acknowledged of conventional financial and cost accounting techniques reflecting companies’ sustainability efforts and providing management with necessary information for sustainable business choices. Information on companies’ environmental performance may be somewhat accessible, but both domestic decision makers and those at the level of public authorities are seldom able to connect environmental information with economic variables and are essentially deprived of environmental cost information. Decision makers do thus not recognize the economic worth of natural resources as asset and the commercial and financial benefit of excellent environmental performance. Beyond ‘goodwill’ efforts, there are a number of market-based incentives for integration with decision making of environmental issues. This article provides an outline of environmental management methods and we evaluate environmental costs in terms of current economic crisis.


2021 ◽  
Vol 3 ◽  
pp. 27-46
Author(s):  
Sonja Utz ◽  
Lara Wolfers ◽  
Anja Göritz

In times of the COVID-19 pandemic, difficult decisions such as the distribution of ventilators must be made. For many of these decisions, humans could team up with algorithms; however, people often prefer human decision-makers. We examined the role of situational (morality of the scenario; perspective) and individual factors (need for leadership; conventionalism) for algorithm preference in a preregistered online experiment with German adults (n = 1,127). As expected, algorithm preference was lowest in the most moral-laden scenario. The effect of perspective (i.e., decision-makers vs. decision targets) was only significant in the most moral scenario. Need for leadership predicted a stronger algorithm preference, whereas conventionalism was related to weaker algorithm preference. Exploratory analyses revealed that attitudes and knowledge also mattered, stressing the importance of individual factors.


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