scholarly journals “The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

2021 ◽  
Vol 12 ◽  
Author(s):  
Alessandra Talamo ◽  
Silvia Marocco ◽  
Chiara Tricol
Keyword(s):  
Decision Making ◽  
Design Thinking ◽  
Cultural Psychology ◽  
Value Proposition ◽  
Individual Decision ◽  
Current Application ◽  
Organizational Goals ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Financial Intelligence

Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.

Applying design thinking to the decision-making process: a field study in Swedish local authorities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Local Government ◽  
Design Thinking ◽  
Structured Interviews ◽  
Local Authorities ◽  
Content Type ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Complex Decision

PurposeThis study aims to examine the application of design thinking to complex decision-making processes in local government and to link the design thinking to the theoretical work of leading thinkers in decision-making.Design/methodology/approachThis study uses multiple case studies, including non-participant observation, group discussions, semi-structured interviews, presentations and questionnaires.FindingsFor complex decisions, design thinking can contribute to more effective decision-making by expanding the range of solutions considered, people consulted and involved, sources of data/information and decision tools as well as in achieving buy-in to the eventual decision.Research limitations/implicationsThe principal limitations include that this is one study in one country and in the public sector. There were some practical problems with external factors disrupting two of the cases, but these do not affect the findings. The principal implication is that by adopting a design thinking approach to complex decision-making, the quality of decision-making and decisions can be significantly improved.Practical implicationsWhen it comes to complex decisions, organisations can improve the quality of both their decision-making processes and their decisions by adopting and implementing ideas and insights from design thinking.Social implicationsFor local authorities, a design approach can enhance the quality of the services provided by local authorities to citizens in particular in better meeting the needs of users and other stakeholders and in opening up better lines of communications between officials and citizens.Originality/valueThis research was based on an initiative in Swedish local government and its first implementation in practice. The authors are not aware of any similar study done elsewhere.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

The effect of the cognitive demands of the distraction task on unconscious thought

2014 ◽  
Vol 37 (1) ◽  
pp. 44-45 ◽  
Author(s):  
Laurent Waroquier ◽  
Marlène Abadie ◽  
Olivier Klein ◽  
Axel Cleeremans
Keyword(s):  
Decision Making ◽  
Unconscious Thought ◽  
Cognitive Demands ◽  
The Unconscious ◽  
Unconscious Processes ◽  
Distraction Task ◽  
Complex Decision Making ◽  
Complex Decision

AbstractThe unconscious-thought effect occurs when distraction improves complex decision making. Recent studies suggest that this effect is more likely to occur with low- than high-demanding distraction tasks. We discuss implications of these findings for Newell & Shanks' (N&S's) claim that evidence is lacking for the intervention of unconscious processes in complex decision making.

Sign in / Sign up

Export Citation Format

Share Document