Repositório Colecção:http://hdl.handle.net/11144/47312024-03-29T10:34:48Z2024-03-29T10:34:48ZThe Maturity of the Competitive Intelligence FunctionJoão, Gonçalohttp://hdl.handle.net/11144/47442021-01-12T12:21:42Z2019-01-01T00:00:00ZTítulo: The Maturity of the Competitive Intelligence Function
Autor: João, Gonçalo
Resumo: When identifying or comparing organizations with a competitive intelligence
function, one should first identify their competitive intelligence systems and then
classify the maturity of their competitive intelligence functions. Thispaper presents
four different classification levels based on different focus from different authors.2019-01-01T00:00:00ZInputs and Outputs of the Intelligence Cycle: a highway to shared definitions and a knowledge base.João, Gonçalohttp://hdl.handle.net/11144/47432021-01-12T12:19:11Z2019-01-01T00:00:00ZTítulo: Inputs and Outputs of the Intelligence Cycle: a highway to shared definitions and a knowledge base.
Autor: João, Gonçalo
Resumo: The establishment of a common ground for basicdefinitions and tools of competitive
intelligence to practitioners and academics can be partially achieved by fully
recognize and comprehend the inputs and outputs of the intelligence cycle.
Nevertheless there are some additional issues to address in order to reach the so
looked-for shared definitions and knowledge base.2019-01-01T00:00:00ZVolume Uncertainty in Construction Projects: A Real Options ApproachRibeiro, João A.Pereira, Paulo J.Brandão, Elísio M.http://hdl.handle.net/11144/47412021-01-12T12:15:49Z2019-01-01T00:00:00ZTítulo: Volume Uncertainty in Construction Projects: A Real Options Approach
Autor: Ribeiro, João A.; Pereira, Paulo J.; Brandão, Elísio M.
Resumo: This paper proposes a model aiming to quantify the impact that a specific type of uncertainty
-volume uncertainty- may produce on construction projects’ value and on the optimal bid
price, in the context of bidding competitions. Volume uncertainty is present in most
construction projects since managers do not know, during the bid preparation stage, the exact
volume of work that will be executed during the project’s life cycle. Volume uncertainty leads
to profit uncertainty and hence the model integrates a discrete-time stochastic variable,
designated as “additional value”, i.e., the value that does not directly derive from the
execution of the tasks specified in the bid documents, and which can only be properly
quantified by undertaking an incremental investment in human capital and technology. The
model determines that, even only recurring to the skills of their own experienced staff,
contractors will produce a more competitive bid provided that the expected amount for the
additional profit is greater than zero. However, construction managers often need to hire
specialized firms and highly skilled professionals in order to quantify the expected amount of
additional value and, hence, the impact of such additional value inthe optimal bidding price.
Based on the option to sign the contract and to perform the project by the selected bidder,
identified and evaluated by Ribeiro et al. (2017), the model’s outcome is the threshold value
for this incremental investment. A decision rule is then reached: construction managers
should invest in human capital and technology provided that the cost of such incremental
investment does not exceed the predetermined threshold value.2019-01-01T00:00:00ZControlling Algorithmic Collusion: Short Review of the Literature, Undecidability, and Alternative ApproachesGata, João E.http://hdl.handle.net/11144/47402021-01-12T12:12:27Z2019-01-01T00:00:00ZTítulo: Controlling Algorithmic Collusion: Short Review of the Literature, Undecidability, and Alternative Approaches
Autor: Gata, João E.
Resumo: Algorithms have played an increasingly important role in economic activity, as they becoming
faster and smarter. Together with the increasing useof ever larger data sets, they may lead to
significant changes in the way markets work. These developments have been raising concerns
not only over the rights to privacy and consumers’ autonomy, but also on competition.
Infringements of antitrust laws involving the use of algorithms have occurred in the past.
However, current concerns are of a different nature as they relate to the role algorithms can play
as facilitators of collusive behavior in repeated games, and the role increasingly sophisticated
algorithms can play as autonomous implementers of pricing strategies, learning to collude
without any explicit instructions provided by human agents. In particular, it is recognized that
the use of ‘learning algorithms’ can facilitate tacit collusion and lead to an increased blurring of
borders between tacit and explicit collusion. Several authors who have addressed the
possibilities for achieving tacit collusion equilibrium outcomes by algorithms interacting
autonomously, have also considered some form of ex-ante assessment and regulation over the
type of algorithms used by firms. By using well-known results in the theory of computation, I
show that such option faces serious challenges to its effectiveness due to undecidability results.
Ex-post assessment may be constrained as well. Notwithstanding several challenges face by
current software testing methodologies, competition law enforcement and policy have much to
gain from an interdisciplinary collaboration with computer science and mathematics.2019-01-01T00:00:00Z